Divergences in Color Perception between Deep Neural Networks and Humans

TL;DR

This study evaluates how well deep neural networks mimic human color perception, revealing significant divergences and proposing a wavelet-based model that aligns more closely with human judgments, advancing understanding of perceptual representations.

Contribution

The paper introduces novel experiments to compare DNNs and humans in color perception, and demonstrates that wavelet-based models better predict human color judgments than current DNNs.

Findings

DNNs significantly diverge from human color similarity judgments.

Wavelet-based models outperform DNNs in predicting human color perception.

Training tasks and layer analysis do not substantially improve DNN alignment with human perception.

Abstract

Deep neural networks (DNNs) are increasingly proposed as models of human vision, bolstered by their impressive performance on image classification and object recognition tasks. Yet, the extent to which DNNs capture fundamental aspects of human vision such as color perception remains unclear. Here, we develop novel experiments for evaluating the perceptual coherence of color embeddings in DNNs, and we assess how well these algorithms predict human color similarity judgments collected via an online survey. We find that state-of-the-art DNN architectures $-$ including convolutional neural networks and vision transformers $-$ provide color similarity judgments that strikingly diverge from human color judgments of (i) images with controlled color properties, (ii) images generated from online searches, and (iii) real-world images from the canonical CIFAR-10 dataset. We compare DNN performance against an interpretable and cognitively plausible model of color perception based on wavelet decomposition, inspired by foundational theories in computational neuroscience. While one deep learning model $-$ a convolutional DNN trained on a style transfer task $-$ captures some aspects of human color perception, our wavelet algorithm provides more coherent color embeddings that better predict human color judgments compared to all DNNs we examine. These results hold when altering the high-level visual task used to train similar DNN architectures (e.g., image classification versus image segmentation), as well as when examining the color embeddings of different layers in a given DNN architecture. These findings break new ground in the effort to analyze the perceptual representations of machine learning algorithms and to improve their ability to serve as cognitively plausible models of human vision. Implications for machine learning, human perception, and embodied cognition are discussed.