OASIS: Open-world Adaptive Self-supervised and Imbalanced-aware System
Miru Kim, Mugon Joe, Minhae Kwon

TL;DR
This paper introduces a contrastive pre-training and post-training framework that improves open-world classification, especially for imbalanced datasets, by generating reliable pseudo-labels and optimizing adaptation processes.
Contribution
It presents a novel contrastive-based pre-training and post-training method that effectively handles class imbalance and enhances model robustness in open-world environments.
Findings
Outperforms state-of-the-art adaptation techniques in accuracy
Demonstrates robustness in imbalanced open-world scenarios
Reduces computational overhead with selective activation
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…
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