GENERATIVE MODEL BASED NOISE ROBUST TRAIN-ING FOR UNSUPERVISED DOMAIN ADAPTATION

Abstract

Target domain pseudo-labelling has shown effectiveness in unsupervised domain adaptation (UDA). However, pseudo-labels of unlabeled target domain data are inevitably noisy due to the distribution shift between source and target domains. This paper proposes a generative model-based noise-robust training method (GeMo-NoRT), which eliminates domain shift while mitigating label noise. GeMo-NoRT incorporates a distribution-based class-wise feature augmentation (D-CFA) and a generative-discriminative classifier consistency (GDC), both based on the class-wise target distributions modelled by generative models. D-CFA minimizes the domain gap by augmenting the source data with distributionsampled target features, and trains a noise-robust discriminative classifier by using target domain knowledge from the generative models. GDC regards all the classwise generative models as generative classifiers and enforces a consistency regularization between the generative and discriminative classifiers. It exploits an ensemble of target knowledge from all the generative models to train a noise-robust discriminative classifier and eventually gets theoretically linked to the Ben-David domain adaptation theorem for reducing the domain gap. Extensive experiments on Office-Home, PACS, and Digit-Five show that our GeMo-NoRT achieves stateof-the-art under single-source and multi-source UDA settings.

1. INTRODUCTION

Convolutional neural networks (CNNs) trained by large amounts of training data have achieved remarkable success on a variety of computer vision tasks (Simonyan & Zisserman, 2014; Szegedy et al., 2015; He et al., 2016; Long et al., 2015a; He et al., 2019) . However, when a well-trained CNN model is deployed in a new environment, its performance usually degrades drastically. This is because the test data (of the target domain) is typically from a different distribution from the training data (of source domains). Such distribution mismatch is also known as domain gap. A popular solution to tackling the domain gap issue is unsupervised domain adaptation (UDA) (Gretton et al., 2012; Long et al., 2015b; 2016; Tzeng et al., 2014; Balaji et al., 2019; Xu et al., 2018) . UDA can be divided into two main sub-settings: single-source domain adaptation (SSDA) and multisource domain adaptation (MSDA), according to the number of source domains. Early works have mainly focused on the single-source scenarios (Gretton et al., 2012; Long et al., 2015b; Ganin et al., 2016; Tzeng et al., 2017) . Nevertheless, in real-world applications, the source domain data can be collected from various deployment environments, leading to multiple source setting. MSDA thus has been receiving more attention recently. Most UDA methods, including both SSDA and MSDA, tried to reduce domain gap by domain distribution alignment (Zhao et al., 2018; Xu et al., 2018; Peng et al., 2019; Li et al., 2021c) . Latest methods (Wang et al., 2020; Li et al., 2021a) further utilized class information for class-wise alignment, with pseudo-labels used for unlabeled target data. These methods have shown effectiveness for UDA. However, due to the domain gap, a model trained on the source domains cannot correctly classify all the target instances, leading to target domain pseudo-labels inevitably being noisy. If these noisy labels are directly used as supervision, their negative impact can be amplified or accumulated through iterations. This can even lead to the training corrupted. The noise accumulation problem thus must be addressed. An intuitive solution to noise accumulation is to reduce domain gap. This can indeed reduce label noise. However, in practice, the domain gap cannot be thoroughly eliminated, so the label noise can

