Posts
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Foundations of diffusion networks
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EchoNarrator: Generating natural text explanations for ejection fraction predictions
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Retrieval Augmented Generation with Knowledge Graphs
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Some recent activation functions mimicking ReLU
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MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine
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Advances in model-free reinforcement learning: REDQ, DroQ, CrossQ.
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Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
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UniverSeg: Universal Medical Image Segmentation
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Editing Models with Task Arithmetic
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Deep reinforcement learning from human preferences
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Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey
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Fully Automated, Quality-Controlled Cardiac Analysis From CMR
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DreamerV3: Mastering Diverse Domains through World Models
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A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography
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Representation learning for improved interpretability and classification accuracy of clinical factors from EEG
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Improving Explainability of Disentangled Representations using Multipath-Attribution Mappings
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TractoFormer: A Novel Fiber-level Whole Brain Tractography Analysis Framework Using Spectral Embedding and Vision Transformers
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White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical region Localization via Geometric Deep Learning
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MIDL 2022
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Learning Optimal White Matter Tract Representations from Tractography using a Deep Generative Model for Population Analyses
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ResMLP: Feedforward networks for image classification with data-efficient training
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What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
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Class Anchor Clustering: A Loss for Distance-based Open Set Recognition
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Escaping the Big Data Paradigm with Compact Transformers
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Consistency Regularization for Variational Auto-Encoders
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ComTriplet: Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets
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Self-Supervised MultiModal Versatile Networks
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Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data
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Scanner invariant representations for diffusion MRI harmonization
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Context-aware virtual adversarial training for anatomically-plausible segmentation
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Data-driven discovery of coordinates and governing equations
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From Variational to Deterministic Autoencoders
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Emerging Properties in Self-Supervised Vision Transformers
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The Diffusion-Simulated Connectivity (DiSCo) Challenge
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AAVAE: Augmentation-Augmented Variational Autoencoders
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Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation
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Perceiver: General Perception with Iterative Attention
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Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation
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Simple and Effective VAE Training with Calibrated Decoders
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Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE
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TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
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Uncertainty Estimation Review
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Unadversarial examples: designing objects for robust vision
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Conservative Q-Learning for Offline Reinforcement Learning
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A generative modeling approach for interpreting population-level variability in brain structure
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Dataset distillation
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CLIP : Learning Transferable Visual Models From Natural Language Supervision
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Distributional Reinforcement Learning with Quantile Regression
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Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation
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Intriguing properties of neural networks
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Evasion attacks against machine learning at test time
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Self supervised learning
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FOD-Net: A Deep Learning Method for Fiber Orientation Distribution Angular Super Resolution
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A Distributional Perspective on Reinforcement Learning
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Similarity of Neural Network Representations Revisited
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MedAL: Accurate and Robust Deep Active Learning for Medical Image Analysis
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Fader Networks: Manipulating Images by Sliding Attributes
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Attribute-based regularization of latent spaces for variational auto-encoders
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AlphaGo/AlphaGoZero/AlphaZero/MuZero: Mastering games using progressively fewer priors
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Domain Generalization via Model-Agnostic Learning of Semantic Features
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EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
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AC-VAE: Learning Semantic Representation with VAE for Adaptive Clustering
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A Deep Neural Network's Loss Surface Contains Every Low-dimensional Pattern
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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Diva: Domain invariant variational autoencoders
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Phasic Policy Gradient
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G-SGD: Optimizing ReLU neural networks in its positively scale-invariant space
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Path-SGD: Path-Normalized Optimization in Deep Neural Networks
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Munchausen Reinforcement Learning
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Disentangled Representation Learning in Cardiac Image Analysis
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InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
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Temporal Consistency Objectives Regularize the Learning of Disentangled Representations
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Isolating Sources of Disentanglement in VAEs
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Disentangling by Factorising
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Using Generative Models for Pediatric wbMRI
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Dynamics-Aware Unsupervised Discovery of Skills
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OnceCycleLR: Super-Convergence: Very Fast Training of NeuralNetworks Using Large Learning Rates
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Self-Attention Generative Adversarial Networks
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Accelerating Online Reinforcement Learning with Offline Datasets
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A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging
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What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
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Deep Learning: A philosophical introduction
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Feature-robustness, flatness and generalization error for deep neural networks
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Sharp Minima can Generalize for Deep Nets
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Agent57: Outperforming the human Atari benchmark
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Demystifying Parallel and Distributed Deep Learning
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Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging
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Never Give Up: Learning Directed Exploration Strategies
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An exponential learning rate schedule for deep learning
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Visualizing the Loss Landscape of Neural Nets
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BEAR (Bootstrapping Error Accumulation Reduction)
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Superfast Diffusion Tensor Imaging and Fiber Tractography Using Deep Learning
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Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation
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Representational drift in Recurrent Reinforcement Learning
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Learning Latent Subspaces in Variational Autoencoders
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L2 Regularization versus Batch and Weight Normalization
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PHiSeg - Capturing Uncertainty in Medical Image Segmentation
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Learning Macroscopic Brain Connectomes via Group-Sparse Factorization
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Implementation Matters in Deep RL: A Case Study on PPO and TRPO
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Upside-Down Reinforcement-Learning
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Rigging the Lottery: Making All Tickets Winners
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Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI
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SPECTRAL GRAPH TRANSFORMER NETWORKS FOR BRAIN SURFACE PARCELLATION
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Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
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Single Headed Attention RNN: Stop Thinking With Your Head
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Associative Compression Networks for Representation Learning
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Stealing Machine Learning Models via Prediction APIs
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Entrack: A Data-Driven Maximum-Entropy Approach to Fiber Tractography
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Estimating localized complexity of white-matter wiring with GANs
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Meta-Learning Deep Energy-Based Memory Models
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ETNet - Error Transition Network for Arbitrary Style Transfer
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Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
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Manifold mixup: Encouraging meaningful on-manifold interpolation as a regularizer
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Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors
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ipA-MedGAN: INPAINTING OF ARBITRARILY REGIONS IN MEDICAL MODALITIES
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On the Utility of Learning about Humans for Human-AI Coordination
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Straight to the point: reinforcement learning for user guidance in ultrasound
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Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation
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Learning shape priors for robust cardiac MR segmentation from multi-view images
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Multi-stage prediction networks for data harmonization
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Lookahead Optimizer: k steps forward, 1 step back
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Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
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On Boosting Semantic Street Scene Segmentation with Weak Supervision
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Neural Discrete Representation Learning
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Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection
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Reconciling modern machine learning practice and the bias-variance trade-off
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Emergent Tool Use from Multi-Agent Interaction
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State-Regularized Recurrent Neural Networks
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Synergistic Image and Feature Adaptation Towards Cross-Modality Domain Adaptation for Medical Image Segmentation
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State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
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Reproducible White Matter Tract Segmentation Using 3D U-Net on a Large-scale DTI Dataset
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Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders
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A Generalized Framework for Population Based Training
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Evaluating the Search Phase of Neural Architecture Search
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Don't Worry About the Weather Unsupervised Condition-Dependent Domain Adaptation
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Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences
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Cortical Surface Parcellation using Spherical Convolutional Neural Networks
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End-to-End Multi-Task Learning with Attention
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d-SNE: Domain Adaptation Using Stochastic Neighborhood Embedding
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Superhuman AI for multiplayer poker
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Iterative Projection and Matching
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Attention Branch Network: Learning of Attention Mechanism for Visual Explanation
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Striving for Simplicity in Off-policy Deep Reinforcement Learning
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Graph-Based Global Reasoning Networks
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Deep Active Learning for Axon-Myelin Segmentation on Histology Data
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Hardness-Aware Deep Metric Learning
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CrDoCo Pixel-Level Domain Transfer With Cross-Domain Consistency
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Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem
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Unsupervised Detection of White Matter Fiber Bundles with Stochastic Neural Networks
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Towards Universal Object Detection by Domain Attention
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Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation
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Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression
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Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks
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Learning Loss for Active Learning
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Continuous Dice Coefficient: a Method for Evaluating Probabilistic Segmentations
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Combined tract segmentation and orientation mapping for bundle-specific tractography
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Residual Policy Learning
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A General and Adaptive Robust Loss Function
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DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks
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Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation
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Batch Policy Gradient Methods for Improving Neural Conversation Models
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Embedded hyper-parameter tuning by Simulated Annealing
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q-Space Novelty Detection with Variational Autoencoders
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Pretraining Deep Actor-Critic Reinforcement Learning Algorithms With Expert Demonstrations
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Multisource and Multitemporal Data Fusion in Remote Sensing
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Speech2Face: Learning the Face Behind a Voice
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Synthesized b0 for diffusion distortion correction (Synb0-DisCo)
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[sketchRNN] A Neural Representation of Sketch Drawings
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The power of ensembles for active learning in image classification
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Generative Adversarial Imitation Learning
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Adversarial Examples Are Not Bugs, They Are Features
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A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets
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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
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DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation
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[Contrastive Loss] Dimensionality Reduction by Learning an Invariant Mapping
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Clipped Proximal Policy Optimization Algorithm
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Learning Structured Output Representation using Deep Conditional Generative Models
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Open Questions about Generative Adversarial Networks
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ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
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Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative Adversarial Network
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GeoSay: A Geometric Saliency for Extracting Buildings in Remote Sensing Images
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Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
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Deep Learning with Compute Canada
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Active learning tutorials
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Exploring Randomly Wired Neural Networks for Image Recognition
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Learning how to Active Learn: A Deep Reinforcement Learning Approach
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3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles
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Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
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Semantic Image Synthesis with Spatially-Adaptive Normalization
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Joint Learning of Brain Lesion and Anatomy Segmentation from Heterogeneous Datasets
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Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss
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Data augmentation using learned transforms for one-shot medical image segmentation
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Prototypical Networks for Few-shot Learning
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Reverse Curriculum Generation for Reinforcement Learning
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PlaNet: Learning Latent Dynamics for Planning from Pixels
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Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI
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Adversarial Autoencoders
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Novel deep learning-based method for prostate segmentation in T2-weighted magnetic resonance imaging
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Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms
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Towards increased trustworthiness of deep learning segmentation methods on cardiac MRI
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Spherical Harmonic Residual Network for Diffusion Signal Harmonization
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An (almost) instant brain atlas segmentation for large-scale studies
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Diversity in Faces
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Character-level Convolutional Networks for Text Classification
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Deep Multi-Structural Shape Analysis: Application to Neuroanatomy
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Learning multiple visual domains with residual adapters
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Harmonizing Diffusion MRI Data Across Multiple Sites and Scanners
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Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing
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A Style-Based Generator Architecture for Generative Adversarial Networks
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Deep Learning with Mixed Supervision for Brain Tumor Segmentation
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h-detach: Modifying the LSTM Gradient Towards Better Optimization
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Adaptive Fusion for RGB-D Salient Object Detection
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Combined Reinforcement Learning via Abstract Representations
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DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography
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PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
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Improved Image Captioning via Policy Gradient optimization of SPIDEr
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ACN: Associative Compression Networks for Representation Learning
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Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects
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Metropolis-Hastings Generative Adversarial Networks
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Go-Explore
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NAG: Network for Adversary Generation
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NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines
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Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder
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Memory Replay GANs
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The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
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From Recognition to Cognition: Visual Commonsense Reasoning
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Structured Pruning of Neural Networks with Budget-Aware Regularization
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OBELISK – One Kernel to Solve Nearly Everything: Unified 3D Binary Convolutions for Image Analysis
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Label Refinement Network for Coarse-to-Fine Semantic Segmentation
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Learning to reinforcement learn
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Low-Shot Learning from Imaginary Data
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Automatically Designing CNN Architectures for Medical Image Segmentation
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Learning Implicit Brain MRI Manifolds with Deep Learning
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Incremental Learning for Semantic Segmentation of Large-Scale Remote Sensing Data
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SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning
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Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography?
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UNet-VAE: A Probabilistic U-Net for Segmentation of Ambiguous Images
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Privacy-preserving generative deep neural networks support clinical data sharing
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Episodic curiosity through reachability
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Cascaded Transforming Multi-task Networks For Abdominal Biometric Estimation from Ultrasound
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SeedNet: Automatic Seed Generation with Deep Reinforcement Learning for Robust Interactive Segmentation
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Progressive Weight Pruning of DNNs using ADMM
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Domain Adaptive Segmentation in Volume Electron Microscopy Imaging
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Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining
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Visual semantic navigation using scene priors
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HeMIS: Hetero-Modal Image Segmentation
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Residual Connections Encourage Iterative Inference
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CBAM: Convolutional Block Attention Module
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A Few Useful Things to Know about Machine Learning
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Exploiting Semantics in Adversarial Training for Image-Level Domain Adaptation
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Rethinking the Value of Network Pruning
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Deep Extreme Cut: From Extreme Points to Object Segmentation
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Divide-and-Conquer Reinforcement Learning
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Tversky loss function for image segmentation using 3D fully convolutional deep networks
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Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network
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PixelSNAIL: An Improved Autoregressive Generative Model
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TreeSegNet: Adaptive Tree CNNs for Subdecimeter Aerial Image Segmentation
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Binarized Neural Networks
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Batch normalization sampling
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Learning Discriminators as Energy Networks in Adversarial Learning
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Exclusive Independent Probability Estimation using Deep 3D Fully Convolutional DenseNets for IsoIntense Infant Brain MRI Segmentation
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Predicting Parameters in Deep Learning
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Large Scale GAN Training for High Fidelity Natural Image Synthesis
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Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
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Feature Selective Networks for Object Detection
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The Elephant in the Room
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Towards the first adversarially robust neural network model on MNIST
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Kronecker Recurrent Units
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CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization
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Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation
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Automatic 3D bi-ventricular segmentation of cardiac images by a shape-constrained multi-task deep learning approach
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PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning
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Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis
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Multi-Modal Convolutional Neural Network for Brain Tumor Segmentation
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Style Augmentation: Data Augmentation via Style Randomization
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On Multiplicative Integration with Recurrent Neural Networks
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MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks
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Dual Attention Network for Scene Segmentation
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DSSLIC: Deep Semantic Segmentation-based Layered Image Compression
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TRAFIC: Fiber Tract Classification Using Deep Learning
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All You Need is Love: Evading Hate-speech Detection
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Learning Shape Priors for Single-View 3D Completion and Reconstruction
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Data-driven Fiber Tractography With Neural Networks
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What do Deep Networks Like to See?
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Attentive Generative Adversarial Network for Raindrop Removal from A Single Image
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Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++
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BlockDrop: Dynamic Inference Paths in Residual Networks
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CNN with Alternately Updated Clique
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Functional Map of the World
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Neural Processes
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Learning a Discriminative Feature Network for Semantic Segmentation
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HAMLET: Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI
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Learning a Spatial Activation Function Restoration
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Learning to Segment Every Thing
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Scale equivariance in CNNs with vector fields
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Mining on Manifolds: Metric Learning without Labels
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Adaptive Neural Trees
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Rethinking the Faster R-CNN Architecture for Temporal Action Localization
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Rotation Equivariant CNNs
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Lightweight Probabilistic Deep Networks
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MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features
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Learning Transferable Deep Models for Land-Use Classification with High-Resolution Remote Sensing Images
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Pose estimation with a Riemannian Geometry loss
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“Learning-Compression” Algorithms for Neural Net Pruning
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Cascade R-CNN: Delving into High Quality Object Detection
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Autofocus layer
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Squeeze-and-Excitation Networks (2017 ImageNet winner)
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Deep Layer Aggregation
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Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation
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Tensor-Train Recurrent Neural Networks for Video Classification
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Burst Denoising with Kernel Prediction Networks
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ClusterNet: Detecting Small Objects in Large Scenes by Exploiting Spatio-Temporal Information
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Star shape Prior
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Normalized Cut Loss for Weakly-supervised CNN Segmentation
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Guide Me: Interacting with Deep Networks
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Deep Networks With Shape Priors for Nucleus Detection
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Domain Shift for Semantic Segmentation
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Learning Longer-term Dependencies in RNNs with Auxiliary Losses
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EnhanceNet : Classification Driven Dynamic Image Enhancement
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Ship wake-detection procedure using conjugate gradient trained artificial neural networks
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The Method of Auxiliary Coordinates
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Beyond the Pixel-Wise Loss for Topology-Aware Delineation
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Simple Does It: Weakly Supervised Instance and Semantic Segmentation
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Budgeted Super Networks
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DOTA: A Large-scale Dataset for Object Detection in Aerial Images
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Siamese LSTM-based fiber structural similarity network (FS2NET) for rotation invariant brain tractography segmentation
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Anatomical Priors for Unsupervised Biomedical Segmentation
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Interactive Image Segmentation with Latent Diversity
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Adversarial Structure Matching Loss for Image Segmentation
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Knowledge Distillation by On-the-Fly Native Ensemble
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Dense labeling of large remote sensing imagery with convolutional neural networks: a simple and faster alternative to stitching output label maps
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Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
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Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features
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Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps
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Indoor Scene Labeling
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Densely Connected Bidirectional LSTM
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Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation
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Monotonic Chunkwise Attention
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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
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Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation
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The Lottery Ticket Hypothesis: Training Pruned Neural Network Architectures
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MEnet: A Metric Expression Network for Salient Object Segmentation
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The Intrinsic Dimension of Objective Landscapes
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Deep Complex Networks
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Select, Attend, and Transfer: Light, Learnable Skip Connections
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Adaptive Cost-sensitive Online Classification
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An Anchor-Free Region Proposal Network for Faster R-CNN based Text Detection Approaches
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Survey: Network compression and speedup
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Multi-Stage Multi-Task Neural Network for Aerial Scene Interpretation and Geolocalization
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Locally Adaptive Learning Loss for Semantic Image Segmentation
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Between-class Learning for Image Classification
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Learning Sparse Structured Ensembles with SG-MCMC and Network Pruning
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GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data
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On Characterizing the Capacity of Neural Networks Using Algebraic Topology
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Optimal Transport for Deep Joint Transfer Learning
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Bayesian Methods for Machine Learning
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On the importance of single directions for generalization
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RetinaNet: Focal Loss for Dense Object Detection
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Knowledg Distillation on Object Detection
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Deep Semantic Face Deblurring
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Generative Adversarial Networks and Probabilistic Graph Models for Hyperspectral Image Classification
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On the insufficiency of existing momentum schemes for Stochastic Optimization
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Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images
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SqueezeNet: AlexNet-Level Accuracy with 50X Fewer Parameters and <0.5MB Model Size
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Recurrent Autoregressive Networks for Online Multi-Object Tracking
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The Building Blocks of Interpretability
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IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Network
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Snapshot Ensembles: Train 1, get M for free
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Vector Field Based Neural Networks
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An Unsupervised Learning Model for Deformable Medical Image Registration
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Learning both Weights and Connections for Efficient Neural Networks
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Building Instance Classification Using Street View Images
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Data Distillation: Towards Omni-Supervised Learning
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Bayesian image quality transfer with CNNs: Exploring uncertainty in dMRI super-resolution
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CS294: Deep Reinforcement Learning
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Reinforcement learning crash course
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Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting
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Neural Architecture Search with Reinforcement Learning
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2D-3D CNN for Cardiac Segmentation
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Learning Aerial Image Segmentation from Online Maps
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FiberNET: An Ensemble Deep Learning Framework for Clustering White Matter Fibers
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Faster R-CNN
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SegFlow
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Two-frame motion estimation based on polynomial expansion
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
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Designing Deep Convolutional Neural Networks for Continuous Object Orientation Estimation
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Clustering with Deep Learning: Taxonomy and New Methods
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Convolutional Recurrent Neural Networks for Hyperspectral Data Classification
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Matching Networks for One Shot Learning
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Gradients explode - Deep Networks are shallow - ResNet explained
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Dynamic Routing Between Capsules
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(NiN) Network In Network
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SB-Net: Sparse Block Network for Fast Inference
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Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network
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Unsupervised Transformation Learning via Convex Relaxations
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Direct White Matter Bundle Segmentation using Stacked U-Nets
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Transforming Auto-encoders
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Deep Reinforcement Learning-based Image Captioning with Embedding Reward
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Full-CNN : Striving for Simplicity: The All Convolutional Net
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Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images using Weakly-Supervised Joint Convolutional Sparse Coding
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Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
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Convolutional neural network architecture for geometric matching
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Aggregated Residual Transformations for Deep Neural Networks
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WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation
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SVP : Surveillance Video Parsing with Single Frame Supervision
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Deep Pyramidal Residual Networks
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Learning from Simulated and Unsupervised Images through Adversarial Training
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DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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Gated Feedback Refinement Network for Dense Image Labeling
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Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation
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Universal adversarial perturbations
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GSNN : The More You Know: Using Knowledge Graphs for Image Classification
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Mask R-CNN
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Growing a Brain: Fine-Tuning by Increasing Model Capacity
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Speed/accuracy trade-offs for modern convolutional object detectors
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Episodic CAMN: Contextual Attention-based Memory Networks With Iterative Feedback For Scene Labeling
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Semi-Supervised Monocular Depth Map Prediction
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PolyNet: A Pursuit of Structural Diversity in Very Deep Networks
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Unsupervised Learning of Depth and Ego-Motion from Video
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Mimicking Very Efficient Network for Object Detection
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Oriented Response Networks
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Multigrid Neural Architectures
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Inverse Compositional Spatial Transformer Networks
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Stacked Generative Adversarial Networks
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FRNN: Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
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Pixelwise Instance Segmentation with a Dynamically Instantiated Network
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LCNN: Lookup-based CNN
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Deep Watershed Transform for Instance Segmentation
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Boundary-aware Instance Segmentation
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Instance-Level Salient Object Segmentation
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Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
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Kernel pooling for Convolutional Neural Network
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FusionSeg: Learning to Combine Motion and Appearance for Fully Automatic Segmentation of Generic Objects in Videos
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Deep Level Sets for Salient Object Detection
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Full Resolution Image Compression with Recurrent Neural Networks
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Scene Parsing through ADE20K dataset
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S3Pool - Pooling with stochastic Spatial Sampling
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A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
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Video Propagation Networks
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Dilated Residual Networks
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Scale-Aware Face Detection
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Evolving Neural Networks Through Augmenting Topologies
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The Marginal Value of Adaptive Gradient Methods
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Tiramisu-Net: The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
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Inception-v3 : Rethinking the Inception Architecture for Computer Vision
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DelugeNet : Deep Networks with Massive and Flexible Cross-layer Information Inflows
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Fast R-CNN
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U-Net + ResNet : The Importance of Skip Connections in Biomedical Image Segmentation
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RCNN : Rich feature hierarchies for accurate object detection and semantic segmentation
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Overfeat : Integrated Recognition, Localization and Detection using Convolutional Networks
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SharpMask : Learning to Refine Object Segments
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DeepMask : Learning to Segment Object Candidates
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Codebook: Background modeling and subtraction by codebook construction
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K-means: Real-Time Adaptive Foreground/Background Segmentation
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Feature Pyramid Networks for Object Detection
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PCA: A Bayesian Computer Vision System for Modeling Human Interactions
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Learning a similarity metric discriminatively, with application to face verification
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DenseNet : Densely Connected Convolutional Networks
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Learning Deconvolution Network for Semantic Segmentation
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KDE: Non-parametric model for background subtraction
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FCNN: Fully Convolutional Networks for Semantic Segmentation
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GMM: Learning patterns of activity using real-time tracking
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LeNet : Gradient-Based Learning Applied to Document Recognition
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CS229: Machine Learning
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GoogLeNet, Inception (Going Deeper with Convolutions)
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Layer Normalization
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Xception: Deep Learning with Depthwise Separable Convolutions
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VggNet: Very Deep Convolutional Networks for Large-Scale Image Recognition
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ResNet: Deep Residual Learning for Image Recognition
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Image-to-Image Translation with Conditional Adversarial Networks
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AlexNet: ImageNet Classification with Deep Convolutional Neural Networks
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You Only Look Once: Unified, Real-Time Object Detection (YOLO)
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A Survey on Deep Learning in Medical Image Analysis
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SSD: Single Shot MultiBox Detector
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U-Net Convolutional Networks for Biomedical Image Segmentation
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V-Net : Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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CS231n: Convolutional Neural Networks for Visual Recognition
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