论文标题
半监督学习的无监督语义聚合和可变形模板匹配
Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning
论文作者
论文摘要
未标记的数据学习最近引起了广泛的关注。但是，仅仅没有监督的学习才能提取预期的高级语义功能仍然难以捉摸。同时，半监督学习（SSL）在利用几个样本方面表现出了有希望的未来。在本文中，我们将两者结合起来，提出了一个无监督的语义聚合和SSL的可变形模板匹配（USADTM）框架，该框架致力于使用很少的标记数据来改善分类性能，然后降低数据注释的成本。具体而言，探索了基于三重态信息（T-MI）损失的无监督语义聚集，以生成未标记数据的语义标签。然后，通过标记数据的监督将语义标签与实际类对齐。此外，将存储标记样品的功能池动态更新，以分配未标记数据的代理标签，该标签被用作跨凝结最小化的目标。四个标准半监督学习基准的大量实验和分析验证了USADTM可以达到最佳性能（例如，CIFAR-10的90.46 $ \％$ $精度，具有40个标签和95.20 $ \％$ $ $ \％$精度，具有250个标签）。该代码在https://github.com/taohan10200/usadtm上发布。
Unlabeled data learning has attracted considerable attention recently. However, it is still elusive to extract the expected high-level semantic feature with mere unsupervised learning. In the meantime, semi-supervised learning (SSL) demonstrates a promising future in leveraging few samples. In this paper, we combine both to propose an Unsupervised Semantic Aggregation and Deformable Template Matching (USADTM) framework for SSL, which strives to improve the classification performance with few labeled data and then reduce the cost in data annotating. Specifically, unsupervised semantic aggregation based on Triplet Mutual Information (T-MI) loss is explored to generate semantic labels for unlabeled data. Then the semantic labels are aligned to the actual class by the supervision of labeled data. Furthermore, a feature pool that stores the labeled samples is dynamically updated to assign proxy labels for unlabeled data, which are used as targets for cross-entropy minimization. Extensive experiments and analysis across four standard semi-supervised learning benchmarks validate that USADTM achieves top performance (e.g., 90.46$\%$ accuracy on CIFAR-10 with 40 labels and 95.20$\%$ accuracy with 250 labels). The code is released at https://github.com/taohan10200/USADTM.
