  Deep networks for visual recognition are known to leverage "easy to
recognise" portions of objects such as faces and distinctive texture patterns.
The lack of a holistic understanding of objects may increase fragility and
overfitting. In recent years, several papers have proposed to address this
issue by means of occlusions as a form of data augmentation. However, successes
have been limited to tasks such as weak localization and model interpretation,
but no benefit was demonstrated on image classification on large-scale
datasets. In this paper, we show that, by using a simple technique based on
batch augmentation, occlusions as data augmentation can result in better
performance on ImageNet for high-capacity models (e.g., ResNet50). We also show
that varying amounts of occlusions used during training can be used to study
the robustness of different neural network architectures.
