Bio-inspired Min-Nets Improve the Performance and Robustness of Deep Networks
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.
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Methods
1x1 Convolution •
Average Pooling •
Batch Normalization •
Bottleneck Residual Block •
Concatenated Skip Connection •
Convolution •
Dense Block •
Dense Connections •
DenseNet •
Dropout •
Global Average Pooling •
Kaiming Initialization •
Max Pooling •
ReLU •
Residual Block •
Residual Connection •
ResNet •
Softmax
