OASIS: Open-world Adaptive Self-supervised and Imbalanced-aware System

The expansion of machine learning into dynamic environments presents challenges in handling open-world problems where label shift, covariate shift, and unknown classes emerge concurrently. Post-training methods have been explored to address these challenges, adapting models to newly emerging data. However, these methods struggle when the initial pre-training is performed on class-imbalanced datasets, limiting generalization to minority classes. To address this, we propose OASIS, an Open-world Adaptive Self-supervised and Imbalanced-aware System. OASIS consists of two learning phases: pre-training and post-training. The pre-training phase aims to improve the classification performance of samples near class boundaries via a novel borderline sample refinement step. Notably, the borderline sample refinement step critically improves the robustness of the decision boundary in the representation space. Through this robustness of the pre-trained model, OASIS generates reliable pseudo-labels, adapting the model against open-world problems in the post-training phase. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art post-training techniques in both accuracy and efficiency across diverse open-world scenarios.