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

• URL: http://arxiv.org/abs/2508.16656v1
• Date: Wed, 20 Aug 2025 08:09:05 GMT
• Title: OASIS: Open-world Adaptive Self-supervised and Imbalanced-aware System
• Authors: Miru Kim, Mugon Joe, Minhae Kwon,
• Abstract summary: We propose a method that effectively handles open-world problems even when pre-training is conducted on imbalanced data.<n>Our contrastive-based pre-training approach enhances classification performance, particularly for underrepresented classes.<n>Our method significantly outperforms state-of-the-art adaptation techniques in both accuracy and efficiency across diverse open-world scenarios.
• Score: 5.085064777896467
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The expansion of machine learning into dynamic environments presents challenges in handling open-world problems where label shift, covariate shift, and unknown classes emerge. 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 a method that effectively handles open-world problems even when pre-training is conducted on imbalanced data. Our contrastive-based pre-training approach enhances classification performance, particularly for underrepresented classes. Our post-training mechanism generates reliable pseudo-labels, improving model robustness against open-world problems. We also introduce selective activation criteria to optimize the post-training process, reducing unnecessary computation. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art adaptation techniques in both accuracy and efficiency across diverse open-world scenarios.

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