Semi-supervised incremental domain generalization learning based on causal invariance

In recent years, semi-supervised learning (SSL) methods based on pseudo-labeling algorithms have been widely applied and achieved significant success. However, most existing deep semi-supervised learning methods suffer from the problem of distribution shift between the source and target domains, as well as the issue of “cognitive bias” in pseudo labeling algorithms, where the model's errors are difficult to rectify as they accumulate through the pseudo-labeling process. This paper introduces the concept of causal invariance and proposes an incremental repeated labeling strategy with a high confidence threshold to enhance the utilization of unlabeled samples. It effectively solves the issue of distribution discrepancy between the source and target domains in the field of semi-supervised learning, as well as the problem of pseudo label "cognitive bias", thus improving the accuracy of the model. Extensive experiments on CIFAR-10, CIFAR-100, SVHN, STL-10, PACS and VLCS demonstrate that semi-supervised incremental models based on causal invariance have a significant improvement in domain generalization ability compared with state-of-the-art methods.