Progressive Feature Alignment for Unsupervised Domain Adaptation

TL;DR

This paper introduces PFAN, a progressive feature alignment network for unsupervised domain adaptation that effectively aligns features across domains by exploiting intra-class variation and iterative training strategies, outperforming existing methods.

Contribution

The paper proposes a novel PFAN framework with EHTS and APA strategies, and a temperature-based method to improve domain alignment and reduce error accumulation in UDA.

Findings

PFAN outperforms state-of-the-art on three UDA datasets.

The EHTS and APA strategies improve class-level alignment.

Temperature scaling enhances source classifier performance.

Abstract

Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a fully-unlabeled target domain. To tackle this task, recent approaches resort to discriminative domain transfer in virtue of pseudo-labels to enforce the class-level distribution alignment across the source and target domains. These methods, however, are vulnerable to the error accumulation and thus incapable of preserving cross-domain category consistency, as the pseudo-labeling accuracy is not guaranteed explicitly. In this paper, we propose the Progressive Feature Alignment Network (PFAN) to align the discriminative features across domains progressively and effectively, via exploiting the intra-class variation in the target domain. To be specific, we first develop an Easy-to-Hard Transfer Strategy (EHTS) and an Adaptive Prototype Alignment (APA) step to train our model iteratively and alternatively. Moreover, upon observing that a good domain adaptation usually requires a non-saturated source classifier, we consider a simple yet efficient way to retard the convergence speed of the source classification loss by further involving a temperature variate into the soft-max function. The extensive experimental results reveal that the proposed PFAN exceeds the state-of-the-art performance on three UDA datasets.