CAKE: CAUSAL AND COLLABORATIVE PROXY-TASKS LEARNING FOR SEMI-SUPERVISED DOMAIN ADAPTA-TION

Abstract

Semi-supervised domain adaptation (SSDA) adapts a learner to a new domain by effectively utilizing source domain data and a few labeled target samples. It is a practical yet under-investigated research topic. In this paper, we analyze the SSDA problem from two perspectives that have previously been overlooked, and correspondingly decompose it into two key subproblems: robust domain adaptation (DA) learning and maximal cross-domain data utilization. (i) From a causal theoretical view, a robust DA model should distinguish the invariant "concept" (key clue to image label) from the nuisance of confounding factors across domains. To achieve this goal, we propose to generate concept-invariant samples to enable the model to classify the samples through causal intervention, yielding improved generalization guarantees; (ii) Based on the robust DA theory, we aim to exploit the maximal utilization of rich source domain data and a few labeled target samples to boost SSDA further. Consequently, we propose a collaboratively debiasing learning framework that utilizes two complementary semi-supervised learning (SSL) classifiers to mutually exchange their unbiased knowledge, which helps unleash the potential of source and target domain training data, thereby producing more convincing pseudo-labels. Such obtained labels facilitate crossdomain feature alignment and duly improve the invariant concept learning. In our experimental study, we show that the proposed model significantly outperforms SOTA methods in terms of effectiveness and generalisability on SSDA datasets.



. That is, it only aligns the features of labeled target samples and their correlated nearby samples with the corresponding feature clusters in the source domain.



DA) aims to transfer training knowledge to the new domain (target D = D T ) using the labeled data available from the original domain (source D = D S ), which can alleviate the poor generalization of learned deep neural networks when the data distribution significantly deviates from the original domain Wang & Deng (2018); You et al. (2019); Tzeng et al. (2017). In the DA community, recent works Saito et al. (2019) have shown that the presence of few labeled data from the target domain can significantly boost the performance of deep learning-based models. This observation led to the formulation of Semi-Supervised Domain Adaptation (SSDA), which is a variant of Unsupervised Domain Adaptation (UDA) Venkateswara et al. (2017) to facilitate model training with rich labels from D S and a few labeled samples from D T . For the fact that we can easily collect such additional labels on the target data in real-world applications, SSDA has the potential to render the adaptation problem more practical and promising in comparison to UDA. Broadly, most contemporary approaches Ganin et al. (2016); Jiang et al. (2020); Kim & Kim (2020); Yoon et al. (2022) handle the SSDA task based on two domain shift assumptions, where X and Y respectively denote the samples and their corresponding labels: (i) Covariate Shift, P (X |D = D S ) ̸ = P (X |D = D T ); (ii) Conditional Shift, P (Y|X , D = D S ) ̸ = P (Y|X , D = D T ), refers to the difference of conditional label distributions of cross-domain data. Intuitively, one straightforward solution for SSDA is to learn the common features to mitigate the domain shift issues. Further quantitative analyses, however, indicate that the model trained with supervision on a few labeled target samples and labeled source data can just ensure partial cross-domain feature alignment Kim &

