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Min-Nets are inspired by end-stopped cortical cells with units that outputthe minimum of two learned filters. We insert such Min-units intostate-of-the-art deep networks, such as the popular ResNet and DenseNet, andshow that the resulting Min-Nets perform better on the Cifar-10 benchmark.Moreover, we show that Min-Nets are more robust against JPEG compressionartifacts. We argue that the minimum operation is the simplest way ofimplementing an AND operation on pairs of filters and that such AND operationsintroduce a bias that is appropriate given the statistics of natural images.

Bio-inspired Min-Nets Improve the Performance and Robustness of Deep Networks