## Method

The new method adds a reconstruction decoder to the classical encoder-decoder segmentation in order to align source and target encoder features.

## Ynet

The method wants to compensate the domain shift between source and target domain by introducing feature (distribution) similarity metrics. They are introducing a second decoder to the standard encoder-decoder setup which serves to reconstruct the input data which originates from both source and target domain. The complete architecture is trained end-to-end with the following loss function:

Where $$L_r$$ is the reconstruction loss function, in their case is a mean-squared error, $$\hat{x}^{s/t}$$ are reconstructions of the source/target inputs obtained by the auto-encoding sub-network.

The network is initially trained in an unsupervised fashion, after which the reconstruction decoder is discarded.

## Results

• FT: Finetuning baseline.
• MMD: Maximum mean discrepancy.
• Coral: Correlation difference.
• DANN: Distributions in an adversarial setup.