Noisy-Pair Robust Representation Alignment for Positive-Unlabeled Learning

• URL: http://arxiv.org/abs/2510.01278v1
• Date: Tue, 30 Sep 2025 18:22:30 GMT
• Title: Noisy-Pair Robust Representation Alignment for Positive-Unlabeled Learning
• Authors: Hengwei Zhao, Zhengzhong Tu, Zhuo Zheng, Wei Wang, Junjue Wang, Rusty Feagin, Wenzhe Jiao,
• Abstract summary: Negative-Unlabeled (PU) learning aims to train a binary classifier where only limited positive data and abundant unlabeled data are available.<n>We propose NcPU, a non-contrastive PU learning framework that requires no auxiliary information.<n>We show that NcPU achieves substantial improvements over state-of-the-art PU methods across diverse datasets.
• Score: 24.345089357698985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Positive-Unlabeled (PU) learning aims to train a binary classifier (positive vs. negative) where only limited positive data and abundant unlabeled data are available. While widely applicable, state-of-the-art PU learning methods substantially underperform their supervised counterparts on complex datasets, especially without auxiliary negatives or pre-estimated parameters (e.g., a 14.26% gap on CIFAR-100 dataset). We identify the primary bottleneck as the challenge of learning discriminative representations under unreliable supervision. To tackle this challenge, we propose NcPU, a non-contrastive PU learning framework that requires no auxiliary information. NcPU combines a noisy-pair robust supervised non-contrastive loss (NoiSNCL), which aligns intra-class representations despite unreliable supervision, with a phantom label disambiguation (PLD) scheme that supplies conservative negative supervision via regret-based label updates. Theoretically, NoiSNCL and PLD can iteratively benefit each other from the perspective of the Expectation-Maximization framework. Empirically, extensive experiments demonstrate that: (1) NoiSNCL enables simple PU methods to achieve competitive performance; and (2) NcPU achieves substantial improvements over state-of-the-art PU methods across diverse datasets, including challenging datasets on post-disaster building damage mapping, highlighting its promise for real-world applications. Code: Code will be open-sourced after review.

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