论文标题
数据增强与最大 - 凝集转换的混合物进行填充级分类
Data augmentation with mixtures of max-entropy transformations for filling-level classification
论文作者
论文摘要
我们使用针对内容级分类任务的原则数据增强方案解决了测试时间数据中的分配变化问题。在这样的任务中，诸如测试时间容器（杯子或水杯）之类的属性可能与训练数据中所示的属性不同。使用标准增强方案处理此类分配变化很具有挑战性，并改变训练图像以涵盖测试时间实例的属性需要复杂的图像操作。因此，我们使用一系列的最大透镜转换产生了不同的增强，这些转换会创建具有新形状，颜色和光谱特征的样品。我们表明，仅这种原则性的增强方案就可以替代使用转移学习的当前方法，或者可以与转移学习结合使用以提高其性能。
We address the problem of distribution shifts in test-time data with a principled data augmentation scheme for the task of content-level classification. In such a task, properties such as shape or transparency of test-time containers (cup or drinking glass) may differ from those represented in the training data. Dealing with such distribution shifts using standard augmentation schemes is challenging and transforming the training images to cover the properties of the test-time instances requires sophisticated image manipulations. We therefore generate diverse augmentations using a family of max-entropy transformations that create samples with new shapes, colors and spectral characteristics. We show that such a principled augmentation scheme, alone, can replace current approaches that use transfer learning or can be used in combination with transfer learning to improve its performance.
