Domain Adaptation for Structured Output via Discriminative Patch Representations

Understand

Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn supervised models like convolutional neural networks.

• However, models trained on one data domain may not generalize well to other domains without annotations for model finetuning.
• To avoid the labor-intensive process of annotation, we develop a domain adaptation method to adapt the source data to the unlabeled target domain.
• We propose to learn discriminative feature representations of patches in the source domain by discovering multiple modes of patch-wise output distribution through the construction of a clustered space.