Unsupervised Domain Adaptation with Similarity Learning

TL;DR

This paper introduces a novel unsupervised domain adaptation method that uses similarity learning with prototype representations, enabling effective classification without domain-specific feature disentanglement.

Contribution

It proposes a joint end-to-end similarity learning approach with prototype representations for domain adaptation, differing from traditional feature alignment methods.

Findings

Achieves state-of-the-art results in multiple domain adaptation benchmarks.

Demonstrates scalability and simplicity of the similarity learning approach.

Effective in leveraging unlabeled target domain data for classification.

Abstract

The objective of unsupervised domain adaptation is to leverage features from a labeled source domain and learn a classifier for an unlabeled target domain, with a similar but different data distribution. Most deep learning approaches to domain adaptation consist of two steps: (i) learn features that preserve a low risk on labeled samples (source domain) and (ii) make the features from both domains to be as indistinguishable as possible, so that a classifier trained on the source can also be applied on the target domain. In general, the classifiers in step (i) consist of fully-connected layers applied directly on the indistinguishable features learned in (ii). In this paper, we propose a different way to do the classification, using similarity learning. The proposed method learns a pairwise similarity function in which classification can be performed by computing similarity between prototype representations of each category. The domain-invariant features and the categorical prototype representations are learned jointly and in an end-to-end fashion. At inference time, images from the target domain are compared to the prototypes and the label associated with the one that best matches the image is outputed. The approach is simple, scalable and effective. We show that our model achieves state-of-the-art performance in different unsupervised domain adaptation scenarios.