Repaint：通过生成多个训练样本来提高下游视觉任务的泛化能力
Repaint: Improving the Generalization of Down-Stream Visual Tasks by Generating Multiple Instances of Training Examples
摘要：
用于视觉任务的卷积神经网络（CNN）被认为在整个网络深度中学习低级纹理和高级对象属性。本文进一步研究了CNN中的“纹理偏差”。为此，我们重新生成多个实例的训练样本从每个原始图像，通过一个过程，我们称之为“重绘”。重新绘制的示例保留了场景中区域和对象的形状和结构，但使其纹理和颜色多样化。我们的方法可以在不同的日光，季节或天气条件下再生相同的图像，可以具有着色或脱色效果，甚至可以从黑色区域带回一些纹理信息。就地重绘允许我们进一步使用这些重绘的示例来改进CNN的泛化。通过一组广泛的实验，我们证明了重新绘制的示例在训练中的有用性，用于图像分类（ImageNet）和目标检测（COCO）的任务，在不同容量的几种最先进的网络架构中，以及在不同的数据可用性制度中。
英文摘要：
Convolutional Neural Networks (CNNs) for visual tasks are believed to learn both the low-level textures and high-level object attributes, throughout the network depth. This paper further investigates the `texture bias' in CNNs. To this end, we regenerate multiple instances of training examples from each original image, through a process we call `repainting'. The repainted examples preserve the shape and structure of the regions and objects within the scenes, but diversify their texture and color. Our method can regenerate a same image at different daylight, season, or weather conditions, can have colorization or de-colorization effects, or even bring back some texture information from blacked-out areas. The in-place repaint allows us to further use these repainted examples for improving the generalization of CNNs. Through an extensive set of experiments, we demonstrate the usefulness of the repainted examples in training, for the tasks of image classification (ImageNet) and object detection (COCO), over several state-of-the-art network architectures at different capacities, and across different data availability regimes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
