用极少标签实现高精度图像识别，训练仅需几分钟。
SemiOccam: A Robust Semi-Supervised Image Recognition Network Using Sparse Labels
- 通过优化特征与类别间的互信息，构建分层密度分类机制。
- 每类仅4个标签时，在两个数据集上准确率超95%。
- 揭露STL-10数据集漏洞，发布去重版CleanSTL-10供公平研究。
我们提出SemiOccam，一种高效且鲁棒的半监督图像识别网络。现有方法通常依赖复杂训练技巧与架构，需数百小时GPU训练，且在极少量标注数据下泛化能力不足。为此，我们通过优化特征表示与目标类别间的互信息，构建分层混合密度分类机制，压缩冗余信息并保留关键判别成分。实验表明，该方法在三个常用数据集上达到当前最优性能：在两个数据集上使用每类仅4个标注样本时，准确率超过95%；其简单架构使训练时间保持在分钟级。值得注意的是，本文揭示了半监督学习中STL-10数据集长期被忽视的数据泄露问题，并移除重复样本以确保结果可靠。我们公开发布去重后的CleanSTL-10数据集，以促进公平、可复现的研究。代码已开源于https://github.com/Shu1L0n9/SemiOccam。
原文摘要 · Abstract (English)
We present SemiOccam, an image recognition network that leverages semi-supervised learning in a highly efficient manner. Existing works often rely on complex training techniques and architectures, requiring hundreds of GPU hours for training, while their generalization ability with extremely limited labeled data remains to be improved. To address these limitations, we construct a hierarchical mixture density classification mechanism by optimizing mutual information between feature representations and target classes, compressing redundant information while retaining crucial discriminative components. Experimental results demonstrate that our method achieves state-of-the-art performance on three commonly used datasets, with accuracy exceeding 95% on two of them using only 4 labeled samples per class, and its simple architecture keeps training time at the minute level. Notably, this paper reveals a long-overlooked data leakage issue in the STL-10 dataset for semi-supervised learning and removes duplicates to ensure reliable experimental results. We release the deduplicated CleanSTL-10 dataset to facilitate fair and reproducible research. Code available at https://github.com/Shu1L0n9/SemiOccam.
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