  Recent studies revealed that convolutional neural networks do not generalize
well to small image transformations, e.g. rotations by a few degrees or
translations of a few pixels. To improve the robustness to such
transformations, we propose to introduce data augmentation at intermediate
layers of the neural architecture, in addition to the common data augmentation
applied on the input images. By introducing small perturbations to activation
maps (features) at various levels, we develop the capacity of the neural
network to cope with such transformations. We conduct experiments on three
image classification benchmarks (Tiny ImageNet, Caltech-256 and Food-101),
considering two different convolutional architectures (ResNet-18 and
DenseNet-121). When compared with two state-of-the-art stabilization methods,
the empirical results show that our approach consistently attains the best
trade-off between accuracy and mean flip rate.
