Generating shift-invariant neural network feature maps and outputs

A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause a computing device to:

• extract feature values from a digital image utilizing one or more extraction layers of a neural network;
• generate a densely pooled feature map from the feature values utilizing a dense pooling layer of the neural network;
• apply a low-pass filter to the densely pooled feature map to generate a shift-adaptive feature map from the densely pooled feature map;
• downsample the shift-adaptive feature map to generate a downsampled shift-adaptive feature map;
• and
• generate a shift-resilient classification of the digital image from the downsampled shift-adaptive feature map.

A system comprising:

• one or more memory devices comprising a neural network comprising a dense pooling layer, a downsampling layer, and a low-pass filter layer between the dense pooling layer and the downsampling layer;
• one or more computing devices that are configured to cause the system to:
• generate a densely pooled feature map utilizing the dense pooling layer of the neural network;
• generate, utilizing the low-pass filter layer of the neural network, a shift-adaptive feature map from the densely pooled feature map;
• and
• downsample, utilizing the downsampling layer of the neural network, a downsampled shift-adaptive feature map from the shift-adaptive feature map.