# 半监督噪声自适应（SSNA）：从噪声领域迁移知识

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-30 08:00
- AIHOT 分数：52
- AIHOT 链接：https://aihot.virxact.com/items/cmpzhkwut03kaslkp3fb77kdz
- 原文链接：https://arxiv.org/abs/2606.00558

## AI 摘要

半监督噪声自适应（SSNA）提出利用简单分布（如高斯分布）构造的合成噪声领域作为源域，在半监督设置下（仅少量目标样本有标签）提升目标域的泛化。基于该问题，建立了刻画噪声领域影响的目标域泛化界，并提出噪声自适应框架（NAF）。实验表明NAF有效利用噪声领域收紧目标域的泛化界，提升性能。代码已开源。

## 正文

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.