Contrastive Conditional Alignment based on Label Shift Calibration for Imbalanced Domain Adaptation

TL;DR

This paper introduces a novel method called CCA-LSC that effectively handles both covariate and label shifts in imbalanced domain adaptation, improving target domain classification accuracy.

Contribution

The paper proposes a contrastive conditional alignment approach with label shift calibration, addressing limitations of existing methods in imbalanced domain adaptation scenarios.

Findings

Outperforms existing UDA and IDA methods on benchmark datasets.

Effectively handles both covariate and label shifts.

Demonstrates robustness in imbalanced domain adaptation tasks.

Abstract

Many existing unsupervised domain adaptation (UDA) methods primarily focus on covariate shift, limiting their effectiveness in imbalanced domain adaptation (IDA) where both covariate shift and label shift coexist. Recent IDA methods have achieved promising results based on self-training using target pseudo labels. However, under the IDA scenarios, the classifier learned in the source domain will exhibit different decision bias from the target domain. It will potentially make target pseudo labels unreliable, and will further lead to error accumulation with incorrect class alignment. Thus, we propose contrastive conditional alignment based on label shift calibration (CCA-LSC) for IDA, to address both covariate shift and label shift. Initially, our contrastive conditional alignment resolve covariate shift to learn representations with domain invariance and class discriminability, which include domain adversarial learning, sample-weighted moving average centroid alignment and discriminative feature alignment. Subsequently, we estimate the probability distribution of the target domain, and calibrate target sample classification predictions based on label shift metrics to encourage labeling pseudo-labels more consistently with the distribution of real target data. Extensive experiments are conducted and demonstrate that our method outperforms existing UDA and IDA methods on benchmarks with both label shift and covariate shift. Our code is available at https://github.com/ysxcj-hub/CCA-LSC.