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Contrast sensitivity function in deep networks.

Arash Akbarinia1, Yaniv Morgenstern2, Karl R Gegenfurtner1

  • 1Department of Experimental Psychology, University of Giessen, Germany.

Neural Networks : the Official Journal of the International Neural Network Society
|May 8, 2023
PubMed
Summary
This summary is machine-generated.

Deep neural networks exhibit human-like contrast sensitivity functions (CSF), mirroring visual system performance. This suggests networks can model image quality and compression effectively, driven by natural world processing.

Keywords:
Artificial neural networksCSFContrastDeep learningVisual featuresVisual perception

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Area of Science:

  • Computational Neuroscience
  • Computer Vision
  • Psychophysics

Background:

  • The contrast sensitivity function (CSF) is a key measure of visual system performance across species.
  • It quantifies the threshold for detecting sinusoidal gratings at various spatial frequencies.

Purpose of the Study:

  • To investigate if deep neural networks (DNNs) exhibit a human-like CSF.
  • To explore how different training tasks and network architectures influence the emergent CSF in DNNs.

Main Methods:

  • Utilized a 2AFC contrast detection paradigm, mirroring human psychophysics.
  • Extracted features from 240 pretrained DNNs and trained linear classifiers for contrast discrimination tasks.
  • Measured network CSF by detecting sinusoidal gratings in natural images.

Main Results:

  • DNNs demonstrated human-like CSF characteristics in luminance (inverted U-shape) and chromatic (low-pass) channels.
  • Task-dependent variations in CSF shape were observed, with low-level tasks yielding closer matches to human CSF.
  • Human-like CSF emerged across various architectures and processing depths, indicating a generalizable phenomenon.

Conclusions:

  • DNNs faithfully model the human CSF, suggesting suitability for image quality and compression applications.
  • The shape of the CSF in DNNs is influenced by the efficient processing of natural image statistics.
  • Visual representations across all hierarchical levels contribute to the CSF, implying complex visual processing underlies this fundamental function.