  Convolutional neural networks (CNN) are capable of learning robust
representation with different regularization methods and activations as
convolutional layers are spatially correlated. Based on this property, a large
variety of regional dropout strategies have been proposed, such as Cutout,
DropBlock, CutMix, etc. These methods aim to promote the network to generalize
better by partially occluding the discriminative parts of objects. However, all
of them perform this operation randomly, without capturing the most important
region(s) within an object. In this paper, we propose Attentive CutMix, a
naturally enhanced augmentation strategy based on CutMix. In each training
iteration, we choose the most descriptive regions based on the intermediate
attention maps from a feature extractor, which enables searching for the most
discriminative parts in an image. Our proposed method is simple yet effective,
easy to implement and can boost the baseline significantly. Extensive
experiments on CIFAR-10/100, ImageNet datasets with various CNN architectures
(in a unified setting) demonstrate the effectiveness of our proposed method,
which consistently outperforms the baseline CutMix and other methods by a
significant margin.
