Deep Kernel Calibration

Korbinian Brodmann argued that the brain regions with different cytoarchitectures exhibited different cognitive functions, which are widely recognized as "Brodmann areas" in neuroscience. Inspired by this theory, we observe from experiments that the different well-trained convolutional kernels also hold their unique functionalities, indicated by their output activations that highly concentrate on the "different certain ranges". This discovery motivates us to devise a deep kernel-by-kernel feature calibration mechanism that "calibrates" the output distribution of a pre-trained convolutional kernel for a more concentrated activation interval, by eliminating the outlier activations. Towards this end, we develop two dedicated Brodmann calibration forms, termed as Hard Kernel Calibration (HKC) and Soft Kernel Calibration (SKC) that simply filters the overrange activations, or adaptively condense the activations in a weighted manner, respectively. As a flexible plug-and-play module, the proposed calibration demonstrates encouraging results on five benchmarks across sixteen network architectures and also triggers the functionality of significantly enhanced model robustness against adversarial attacks. The developed code is publicly available at https://github.com/tcmyxc/DKC.