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Color illusions also deceive CNNs for low-level vision tasks: Analysis and implications
A Gomez-Villa1, A Martín1, J Vazquez-Corral1
1Dept. Inf. Comm. Tech., Universitat Pompeu Fabra, Barcelona, Spain.
Vision Research
|September 8, 2020
Summary
Convolutional neural networks (CNNs) trained for vision tasks can mimic some human visual illusions but also produce unique ones. Linear analysis reveals similarities due to optimization, while nonlinearities in complex CNNs may diverge from human perception.
Area of Science:
- Computational neuroscience
- Computer vision
- Psychophysics
Background:
- Visual illusions offer insights into human vision science.
- Convolutional neural networks (CNNs) are increasingly used to model visual processing.
Purpose of the Study:
- To investigate similarities and differences between CNNs and human perception regarding visual illusions.
- To analyze the origins of these similarities and differences in CNN architectures.
Main Methods:
- Training CNNs on low-level visual tasks.
- Applying linear eigenanalysis to simple CNNs.
- Comparing CNN-generated illusions with human perception data.
Main Results:
- Simple CNNs, when linearized, exhibit properties similar to human visual systems (e.g., center-surround receptive fields, contrast sensitivity functions).
- Complex, nonlinear CNN architectures may fail to replicate human visual illusions accurately.
- Differences in illusion replication stem from nonlinear behaviors not captured by linear approximations.
Conclusions:
- Low-level visual illusions in simple CNNs may arise from optimization processes, mirroring human vision.
- Complex CNNs, despite their flexibility, might not be ideal for studying human vision due to diverging nonlinear dynamics.
- Caution is advised when using CNNs to model human vision, considering inherent limitations and architectural nonlinearities.
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