Let the Void Be Void: Robust Open-Set Semi-Supervised Learning via Selective Non-Alignment

• URL: http://arxiv.org/abs/2504.12569v3
• Date: Wed, 06 Aug 2025 14:06:10 GMT
• Title: Let the Void Be Void: Robust Open-Set Semi-Supervised Learning via Selective Non-Alignment
• Authors: You Rim Choi, Subeom Park, Seojun Heo, Eunchung Noh, Hyung-Sin Kim,
• Abstract summary: Open-set semi-supervised learning (OSSL) leverages unlabeled data containing both in-distribution (ID) and unknown out-of-distribution (OOD) samples.<n>We introduce selective non-alignment, adding a novel "skip" operator into conventional pull and push operations.<n>Our framework, SkipAlign, selectively skips alignment (pulling) for low-confidence unlabeled samples, retaining only gentle repulsion against ID prototypes.
• Score: 1.8350044465969415
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Open-set semi-supervised learning (OSSL) leverages unlabeled data containing both in-distribution (ID) and unknown out-of-distribution (OOD) samples, aiming simultaneously to improve closed-set accuracy and detect novel OOD instances. Existing methods either discard valuable information from uncertain samples or force-align every unlabeled sample into one or a few synthetic "catch-all" representations, resulting in geometric collapse and overconfidence on only seen OODs. To address the limitations, we introduce selective non-alignment, adding a novel "skip" operator into conventional pull and push operations of contrastive learning. Our framework, SkipAlign, selectively skips alignment (pulling) for low-confidence unlabeled samples, retaining only gentle repulsion against ID prototypes. This approach transforms uncertain samples into a pure repulsion signal, resulting in tighter ID clusters and naturally dispersed OOD features. Extensive experiments demonstrate that SkipAlign significantly outperforms state-of-the-art methods in detecting unseen OOD data without sacrificing ID classification accuracy.

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