  The convolutional neural network (CNN) is vulnerable to degraded images with
even very small variations (e.g. corrupted and adversarial samples). One of the
possible reasons is that CNN pays more attention to the most discriminative
regions, but ignores the auxiliary features when learning, leading to the lack
of feature diversity for final judgment. In our method, we propose to
dynamically suppress significant activation values of CNN by group-wise
inhibition, but not fixedly or randomly handle them when training. The feature
maps with different activation distribution are then processed separately to
take the feature independence into account. CNN is finally guided to learn
richer discriminative features hierarchically for robust classification
according to the proposed regularization. Our method is comprehensively
evaluated under multiple settings, including classification against
corruptions, adversarial attacks and low data regime. Extensive experimental
results show that the proposed method can achieve significant improvements in
terms of both robustness and generalization performances, when compared with
the state-of-the-art methods. Code is available at
https://github.com/LinusWu/TENET_Training.
