Enhancing Semi-supervised Learning with Noisy Zero-shot Pseudolabels

• URL: http://arxiv.org/abs/2502.12584v1
• Date: Tue, 18 Feb 2025 06:41:53 GMT
• Title: Enhancing Semi-supervised Learning with Noisy Zero-shot Pseudolabels
• Authors: Jichan Chung, Irene Y. Chen,
• Abstract summary: We present ZMT (Zero-Shot Multi-Task Learning), a framework that jointly optimize zero-shot pseudo-labels and unsupervised representation learning objectives. Our method introduces a multi-task learning-based mechanism that incorporates pseudo-labels while ensuring robustness to varying pseudo-label quality. Experiments across 8 datasets in vision, language, and audio domains demonstrate that ZMT reduces error by up to 56% compared to traditional SSL methods.
• Score: 3.1614158472531435
• License:
• Abstract: Semi-supervised learning (SSL) leverages limited labeled data alongside abundant unlabeled data to address labeling costs in machine learning. While recent foundation models enable zero-shot inference, attempts to integrate these capabilities into SSL through pseudo-labeling have shown mixed results due to unreliable zero-shot predictions. We present ZMT (Zero-Shot Multi-Task Learning), a framework that jointly optimizes zero-shot pseudo-labels and unsupervised representation learning objectives from contemporary SSL approaches. Our method introduces a multi-task learning-based mechanism that incorporates pseudo-labels while ensuring robustness to varying pseudo-label quality. Experiments across 8 datasets in vision, language, and audio domains demonstrate that ZMT reduces error by up to 56% compared to traditional SSL methods, with particularly compelling results when pseudo-labels are noisy and unreliable. ZMT represents a significant step toward making semi-supervised learning more effective and accessible in resource-constrained environments.

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