无监督解决训练与目标数据类别分布不匹配问题
Unsupervised Learning for Class Distribution Mismatch
- 从无标签数据构建正负样本对，利用扩散模型生成多样训练样本
- 在Tiny-ImageNet上60%分布不匹配时，性能远超有标签方法
- 适合真实场景中缺乏标注数据的类别分布迁移任务
类别分布不匹配（CDM）指训练数据与目标任务间类别分布的差异。现有方法多针对半监督场景，依赖大量标注数据，限制了应用范围。为此，我们提出无监督类别分布不匹配学习（UCDM），通过无标签数据构建正负样本对进行分类器训练。方法随机采样图像，使用扩散模型添加或移除语义类别，合成多样化训练对。同时引入基于置信度的伪标签机制，迭代为高质量真实数据分配伪标签并融入训练。在三个数据集上的实验表明，UCDM显著优于以往半监督方法。尤其在Tiny-ImageNet上60%分布不匹配条件下，无需任何标签即超越OpenMatch（每类40个标签）在已知、未知和新类别上的表现，分别提升35.1%、63.7%和72.5%。
原文摘要 · Abstract (English)
Class distribution mismatch (CDM) refers to the discrepancy between class distributions in training data and target tasks. Previous methods address this by designing classifiers to categorize classes known during training, while grouping unknown or new classes into an "other" category. However, they focus on semi-supervised scenarios and heavily rely on labeled data, limiting their applicability and performance. To address this, we propose Unsupervised Learning for Class Distribution Mismatch (UCDM), which constructs positive-negative pairs from unlabeled data for classifier training. Our approach randomly samples images and uses a diffusion model to add or erase semantic classes, synthesizing diverse training pairs. Additionally, we introduce a confidence-based labeling mechanism that iteratively assigns pseudo-labels to valuable real-world data and incorporates them into the training process. Extensive experiments on three datasets demonstrate UCDM's superiority over previous semi-supervised methods. Specifically, with a 60% mismatch proportion on Tiny-ImageNet dataset, our approach, without relying on labeled data, surpasses OpenMatch (with 40 labels per class) by 35.1%, 63.7%, and 72.5% in classifying known, unknown, and new classes.
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