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