  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.
