FREE LUNCH FOR DOMAIN ADVERSARIAL TRAINING: ENVIRONMENT LABEL SMOOTHING

Abstract

A fundamental challenge for machine learning models is how to generalize learned models for out-of-distribution (OOD) data. Among various approaches, exploiting invariant features by Domain Adversarial Training (DAT) received widespread attention. Despite its success, we observe training instability from DAT, mostly due to over-confident domain discriminator and environment label noise. To address this issue, we proposed Environment Label Smoothing (ELS), which encourages the discriminator to output soft probability, which thus reduces the confidence of the discriminator and alleviates the impact of noisy environment labels. We demonstrate, both experimentally and theoretically, that ELS can improve training stability, local convergence, and robustness to noisy environment labels. By incorporating ELS with DAT methods, we are able to yield the state-of-art results on a wide range of domain generalization/adaptation tasks, particularly when the environment labels are highly noisy.

1. INTRODUCTION

Despite being empirically effective on visual recognition benchmarks (Russakovsky et al., 2015) , modern neural networks are prone to learning shortcuts that stem from spurious correlations (Geirhos et al., 2020) , resulting in poor generalization for out-of-distribution (OOD) data. A popular thread of methods, minimizing domain divergence by Domain Adversarial Training (DAT) (Ganin et al., 2016) , has shown better domain transfer performance, suggesting that it is potential to be an effective candidate to extract domain-invariant features. Despite its power for domain adaptation and domain generalization, DAT is known to be difficult to train and converge (Roth et al., 2017; Jenni & Favaro, 2019; Arjovsky & Bottou, 2017; Sønderby et al., 2016) . 



* Work done during an internship at Alibaba Group. † Work done at Alibaba Group, and now affiliated with Twitter.



Figure 1: A motivating example of ELS with 3 domains on the VLCS dataset.

