Robust Image Classification Using A Low-Pass Activation Function and DCT Augmentation

Convolutional Neural Network's (CNN's) performance disparity on clean and corrupted datasets has recently been noticed. In this work, we analyse common corruptions from a frequency perspective, i.e., High Frequency corruptions or HFc (e.g., noise) and Low Frequency corruptions or LFc (e.g., blur). A common signal processing solution to HFc is low-pass filtering. Intriguingly, the de-facto Activation Function (AF) used in modern CNNs, i.e., ReLU does not have any filtering mechanism resulting in unstable performance on HFc. In this work, we propose a family of novel AFs with low-pass filtering to improve robustness against HFc (we call it Low-Pass ReLU or LP-ReLU). To deal with LFc, we further enhance the AFs with Discrete Cosine Transform (DCT) based augmentation. LP-ReLU coupled with DCT augmentation, enables a deep network to tackle a variety of corruptions. We evaluate our method's performance on CIFAR-10-C and Tiny ImageNet-C datasets and achieve improvements of 5.1% and 7.2% in accuracy respectively compared to the State-Of-The-Art (SOTA). We further evaluate our method's performance stability on a variety of perturbations available in CIFAR-10-P and Tiny ImageNet-P. We also achieve new SOTA results in these experiments. We also devise a decision space visualisation process to further strengthen the understanding regarding CNN's lack of robustness against corrupted data.