论文标题
交换语义内容以混合图像
Swapping Semantic Contents for Mixing Images
论文作者
论文摘要
深度建筑已证明能够解决许多任务提供足够数量的标记数据。实际上，可用标记的数据的数量已成为低标签设置（例如半监督学习）中的主要瓶颈。混合数据增强通常不会产生新标记的样品，因为不加选择的混合内容会产生类样品之间的样本。在这项工作中，我们介绍了SCIMIX框架，该框架可以学习生成器将语义样式代码嵌入图像背景中，并获得了用于数据增强的新混合方案。然后，我们证明Scimix产生了新型混合样品，这些样本从其非语义父母那里继承了许多特征。之后，我们验证这些样本可用于改善诸如卑鄙的老师或FixMatch之类的性能半监督框架，甚至在小标记的数据集中进行完全监督的学习。
Deep architecture have proven capable of solving many tasks provided a sufficient amount of labeled data. In fact, the amount of available labeled data has become the principal bottleneck in low label settings such as Semi-Supervised Learning. Mixing Data Augmentations do not typically yield new labeled samples, as indiscriminately mixing contents creates between-class samples. In this work, we introduce the SciMix framework that can learn to generator to embed a semantic style code into image backgrounds, we obtain new mixing scheme for data augmentation. We then demonstrate that SciMix yields novel mixed samples that inherit many characteristics from their non-semantic parents. Afterwards, we verify those samples can be used to improve the performance semi-supervised frameworks like Mean Teacher or Fixmatch, and even fully supervised learning on a small labeled dataset.
