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arXiv · 2504.12569

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

You Rim Choi·Subeom Park·Seojun Heo·Eunchung Noh·Hyung-Sin Kim
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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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You Rim Choi, Subeom Park, Seojun Heo, Eunchung Noh, Hyung-Sin Kim. 2026-01-16. Let the Void Be Void: Robust Open-Set Semi-Supervised Learning via Selective Non-Alignment. https://arxiv.org/abs/2504.12569

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