Exploiting Local Feature Patterns for Unsupervised Domain Adaptation

TL;DR

This paper introduces a novel unsupervised domain adaptation method that leverages local feature patterns and their multi-mode statistics to improve domain-invariant representation learning, outperforming existing holistic approaches.

Contribution

It proposes a new approach that jointly aligns holistic and local feature statistics, emphasizing the transferability of local feature patterns for better domain adaptation.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Effectively alleviates negative transfer.

Enhances fine-grained feature alignment.

Abstract

Unsupervised domain adaptation methods aim to alleviate performance degradation caused by domain-shift by learning domain-invariant representations. Existing deep domain adaptation methods focus on holistic feature alignment by matching source and target holistic feature distributions, without considering local features and their multi-mode statistics. We show that the learned local feature patterns are more generic and transferable and a further local feature distribution matching enables fine-grained feature alignment. In this paper, we present a method for learning domain-invariant local feature patterns and jointly aligning holistic and local feature statistics. Comparisons to the state-of-the-art unsupervised domain adaptation methods on two popular benchmark datasets demonstrate the superiority of our approach and its effectiveness on alleviating negative transfer.