Robust deep learning object recognition models rely on low frequency information in natural images
Fig 1
Schematic of neural similarity regularization.
Machine learning models are trained so that the stimuli manifold in the model feature space resembles the manifold in the neural response space. The most simple version considers only pairwise relationships among stimuli instances. Two images that are close in the neural response space should also be close in the model feature space.