Mind the Discriminability: Asymmetric Adversarial Domain Adaptation

Abstract

Adversarial domain adaptation has made tremendous success by learning domain-invariant feature representations. However, conventional adversarial training pushes two domains together and brings uncertainty to feature learning, which deteriorates the discriminability in the target domain. In this paper, we tackle this problem by designing a simple yet effective scheme, namely Asymmetric Adversarial Domain Adaptation (AADA). We notice that source features preserve great feature discriminability due to full supervision, and therefore a novel asymmetric training scheme is designed to keep the source features fixed and encourage the target features approaching to the source features, which best preserves the feature discriminability learned from source labeled data. This is achieved by an autoencoder-based domain discriminator that only embeds the source domain, while the feature extractor learns to deceive the autoencoder by embedding the target domain. Theoretical justifications corroborate that our method minimizes the domain discrepancy and spectral analysis is employed to quantize the improved feature discriminability. Extensive experiments on several benchmarks validate that our method outperforms existing adversarial domain adaptation methods significantly and demonstrates robustness with respect to hyper-parameter sensitivity.