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

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.

centroIDA: Cross-Domain Class Discrepancy Minimization Based on Accumulative Class-Centroids for Imbalanced Domain Adaptation

Unsupervised Domain Adaptation (UDA) approaches address the covariate shift problem by minimizing the distribution discrepancy between the source and target domains, assuming that the label distribution is invariant across domains. However, in the imbalanced domain adaptation (IDA) scenario, covariate and long-tailed label shifts both exist across domains. To tackle the IDA problem, some current research focus on minimizing the distribution discrepancies of each corresponding class between source and target domains. Such methods rely much on the reliable pseudo labels' selection and the feature distributions estimation for target domain, and the minority classes with limited numbers makes the estimations more uncertainty, which influences the model's performance. In this paper, we propose a cross-domain class discrepancy minimization method based on accumulative class-centroids for IDA (centroIDA). Firstly, class-based re-sampling strategy is used to obtain an unbiased classifier on source domain. Secondly, the accumulative class-centroids alignment loss is proposed for iterative class-centroids alignment across domains. Finally, class-wise feature alignment loss is used to optimize the feature representation for a robust classification boundary. A series of experiments have proved that our method outperforms other SOTA methods on IDA problem, especially with the increasing degree of label shift.