DIVIDE TO ADAPT: MITIGATING CONFIRMATION BIAS FOR DOMAIN ADAPTATION OF BLACK-BOX PREDIC-TORS

Abstract

aims to learn a model on an unlabeled target domain supervised by a black-box predictor trained on a source domain. It does not require access to both the source-domain data and the predictor parameters, thus addressing the data privacy and portability issues of standard domain adaptation methods. Existing DABP approaches mostly rely on knowledge distillation (KD) from the black-box predictor, i.e., training the model with its noisy target-domain predictions, which however inevitably introduces the confirmation bias accumulated from the prediction noises and leads to degrading performance. To mitigate such bias, we propose a new strategy, divide-to-adapt, that purifies cross-domain knowledge distillation by proper domain division. This is inspired by an observation we make for the first time in domain adaptation: the target domain usually contains easy-to-adapt and hard-to-adapt samples that have different levels of domain discrepancy w.r.t. the source domain, and deep models tend to fit easyto-adapt samples first. Leveraging easy-to-adapt samples with less noise can help KD alleviate the negative effect of prediction noises from black-box predictors. In this sense, the target domain can be divided into an easy-to-adapt subdomain with less noise and a hard-to-adapt subdomain at the early stage of training. Then the adaptation is achieved by semi-supervised learning. We further reduce distribution discrepancy between subdomains and develop weak-strong augmentation strategy to filter the predictor errors progressively. As such, our method is a simple yet effective solution to reduce error accumulation in cross-domain knowledge distillation for DABP. Moreover, we prove that the target error of DABP is bounded by the noise ratio of two subdomains, i.e., the confirmation bias, which provides the theoretical justifications for our method. Extensive experiments demonstrate our method achieves state of the art on all DABP benchmarks, outperforming the existing best approach by 9.5% on VisDA-17, and is even comparable with the standard domain adaptation methods that use the source-domain data 1 .

1. INTRODUCTION

Unsupervised domain adaptation (UDA) (Pan & Yang, 2009) aims to transfer knowledge from a labeled source domain to an unlabeled target domain and has wide applications (Tzeng et al., 2015; Hoffman et al., 2018; Zou et al., 2021) . However, UDA methods require to access the sourcedomain data, thus raising concerns about data privacy and portability issues. To solve them, Domain Adaptation of Black-box Predictors (DABP) (Liang et al., 2022) was introduced recently, which aims to learn a model with only the unlabeled target-domain data and a black-box predictor trained on the source domain, e.g., an API in the cloud, to avoid the privacy and safety issues from the leakage of data and model parameters.

