Self-Supervised Learning for Domain Generalization With a Multi-Classifier Ensemble Approach

Abstract

Domain generalization poses significant challenges, particularly as models must generalize effectively to unseen target domains after training on multiple source domains. Traditional approaches typically aim to minimize domain discrepancies; however, they often fall short when handling complex data variations and class imbalance. In this paper, we propose an innovative model, the self-supervised learning multi-classifier ensemble (SSL-MCE), to address these limitations. SSL-MCE integrates self-supervised learning within a dynamic multi-classifier ensemble framework, leveraging ResNet as a shared feature extraction backbone. By combining four distinct classifiers, it captures diverse and complementary features, thereby enhancing adaptability to new domains. A self-supervised rotation prediction task enables SSL-MCE to focus on intrinsic data structures rather than domain-specific details, learning robust domain-invariant features. To mitigate class imbalance, we incorporate adaptive focal attention loss (AFAL), which dynamically emphasizes challenging and rare instances, ensuring improved accuracy on difficult samples. Furthermore, SSL-MCE adopts a dynamic loss-based weighting scheme to prioritize more reliable classifiers in the final prediction. Extensive experiments conducted on public benchmark datasets, including PACS and DomainNet, indicate that SSL-MCE outperforms state-of-the-art methods, achieving superior generalization and resource efficiency through its streamlined ensemble framework.