Let the Void Be Void: Robust Open-Set Semi-Supervised Learning via Selective Non-Alignment
This addresses the challenge of robustly handling unknown data in semi-supervised learning, which is incremental as it builds on contrastive learning methods.
The paper tackled the problem of open-set semi-supervised learning, where unlabeled data includes both in-distribution and out-of-distribution samples, by introducing selective non-alignment to avoid discarding uncertain samples or forcing alignment, resulting in significantly improved detection of unseen OOD data without compromising ID classification accuracy.
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.