

Abstract

Given multiple source datasets with labels, how can we train a target model with no labeled data? Multi-source domain adaptation (MSDA) aims to train a model using multiple source datasets different from a target dataset in the absence of target data labels. MSDA is a crucial problem applicable to many practical cases where labels for the target data are unavailable due to privacy issues. Existing MSDA frameworks are limited since they align data without considering conditional distributions p(x|y) of each domain. They also do not fully utilize the target data without labels, and rely on limited feature extraction with a single extractor. In this paper, we propose MULTI-EPL, a novel method for multi-source domain adaptation. MULTI-EPL exploits label-wise moment matching to align conditional distributions p(x|y), uses pseudolabels for the unavailable target labels, and introduces an ensemble of multiple feature extractors for accurate domain adaptation. Extensive experiments show that MULTI-EPL provides the state-of-the-art performance for multi-source domain adaptation tasks in both of image domains and text domains.

1. INTRODUCTION

Given multiple source datasets with labels, how can we train a target model with no labeled data? A large training data are essential for training deep neural networks. Collecting abundant data is unfortunately an obstacle in practice; even if enough data are obtained, manually labeling those data is prohibitively expensive. Using other available or much cheaper datasets would be a solution for these limitations; however, indiscriminate usage of other datasets often brings severe generalization error due to the presence of dataset shifts (Torralba & Efros (2011) ). Unsupervised domain adaptation (UDA) tackles these problems where no labeled data from the target domain are available, but labeled data from other source domains are provided. Finding out domain-invariant features has been the focus of UDA since it allows knowledge transfer from the labeled source dataset to the unlabeled target dataset. While the above-mentioned approaches consider one single source, we address multi-source domain adaptation (MSDA), which is very crucial and more practical in real-world applications as well as more challenging. MSDA is able to bring significant performance enhancement by virtue of accessibility to multiple datasets as long as multiple domain shift problems are resolved. Previous works have extensively presented both theoretical analysis (Ben-David et al. ( 2010 2018)) build adversarial networks for each source domain to generate features domain-invariant enough to confound domain classifiers. However, these approaches do not encompass the shifts among source domains, counting only shifts between source and target domain. M 3 SDA (Peng et al. ( 2019)) adopts moment matching strategy but makes the unrealistic assumption that matching the marginal probability p(x) would guarantee the alignment of the conditional probability p(x|y). Most of these methods also do not fully exploit the knowledge of target



There have been many efforts to transfer knowledge from a single source domain to a target one. Most recent frameworks minimize the distance between two domains by deep neural networks and distance-based techniques such as discrepancy regularizers (Long et al. (2015; 2016; 2017)), adversarial networks (Ganin et al. (2016); Tzeng et al. (2017)), and generative networks (Liu et al. (2017); Zhu et al. (2017); Hoffman et al. (2018b)).

); Mansour et al. (2008); Crammer et al. (2008); Hoffman et al. (2018a); Zhao et al. (2018); Zellinger et al. (2020)) and models (Zhao et al. (2018); Xu et al. (2018); Peng et al. (2019)) for MSDA. MDAN (Zhao et al. (2018)) and DCTN (Xu et al. (

