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