Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain

2026-05-30

AI Summary

This work proposes the semi-supervised noise adaptation (SSNA) problem, which aims to enhance model generalization in semi-supervised settings by leveraging a synthetic noise domain—such as Gaussian noise—lacking semantic information as a surrogate source domain, using only a small number of labeled target-domain samples. To address this challenge, the authors introduce the Noise Adaptation Framework (NAF), which, for the first time, incorporates synthetic noise domains into semi-supervised transfer learning and derives a theoretical generalization bound to guide algorithm design. Empirical results demonstrate that NAF effectively exploits knowledge from the noise domain to tighten the generalization bound on the target domain, leading to significant performance improvements over existing semi-supervised learning methods across multiple benchmarks.

Abstract

Transfer learning aims to facilitate the learning of a target domain by transferring knowledge from a source domain. The source domain typically contains semantically meaningful samples (*e.g.*, images) to facilitate effective knowledge transfer. However, a recent study observes that the noise domain constructed from simple distributions (*e.g.*, Gaussian distributions) can serve as a surrogate source domain in the semi-supervised setting, where only a small proportion of target samples are labeled while most remain unlabeled. Based on this surprising observation, we formulate a novel problem termed *Semi-Supervised Noise Adaptation* (SSNA), which aims to leverage a synthetic noise domain to improve the generalization of the target domain. To address this problem, we first establish a generalization bound characterizing the effect of the noise domain on generalization, based on which we propose a Noise Adaptation Framework (NAF). Extensive experiments demonstrate that NAF effectively leverages the noise domain to tighten the generalization bound of the target domain, leading to improved performance. The codes are available at https://github.com/AIResearch-Group/SSNA.

Problem

• Semi-Supervised Learning
• Noise Domain
• Transfer Learning
• Domain Adaptation
• Generalization

Innovation

• Semi-Supervised Noise Adaptation
• Noise Domain
• Transfer Learning
• Generalization Bound
• Noise Adaptation Framework