Contrastive Learning with Positive Augmentation in Open World Test-Time Training

Test-time training (TTT) seeks to adapt models trained on a source domain to unseen target domains during testing, addressing challenges of distribution shifts. While previous studies have concentrated on improving model performance against common corruptions and exploring robustness in real-world scenarios, the issue of generalizing pre-trained models to target domains comprising both corrupted and semantically distinct data remains underexplored. This paper introduces a contrastive test-time training approach that incorporates sample identification and discriminability regularization to address these challenges. We utilize entropy as an indicator to selectively filter strong out-of-distribution (OOD) data from test samples, enhancing model adaptation to target corrupted samples. By employing contrastive representation learning, our approach improves feature effectiveness and model robustness. Additionally, we define a feature diversity regularization loss to balance the model's transfer and discriminative capabilities. Experimental results demonstrate that our method surpasses state-of-the-art techniques in three evaluation metrics across multiple benchmark datasets.