On the relation between statistical learning and perceptual distances
- URL: http://arxiv.org/abs/2106.04427v1
- Date: Tue, 8 Jun 2021 14:56:56 GMT
- Title: On the relation between statistical learning and perceptual distances
- Authors: Alexander Hepburn and Valero Laparra and Raul Santos-Rodriguez and
Johannes Ball\'e and Jes\'us Malo
- Abstract summary: We show that perceptual sensitivity is correlated with the probability of an image in its close neighborhood.
We also explore the relation between distances induced by autoencoders and the probability distribution of the data used for training them.
- Score: 61.25815733012866
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: It has been demonstrated many times that the behavior of the human visual
system is connected to the statistics of natural images. Since machine learning
relies on the statistics of training data as well, the above connection has
interesting implications when using perceptual distances (which mimic the
behavior of the human visual system) as a loss function. In this paper, we aim
to unravel the non-trivial relationship between the probability distribution of
the data, perceptual distances, and unsupervised machine learning. To this end,
we show that perceptual sensitivity is correlated with the probability of an
image in its close neighborhood. We also explore the relation between distances
induced by autoencoders and the probability distribution of the data used for
training them, as well as how these induced distances are correlated with human
perception. Finally, we discuss why perceptual distances might not lead to
noticeable gains in performance over standard Euclidean distances in common
image processing tasks except when data is scarce and the perceptual distance
provides regularization.
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