Systematic comparison of color representations between humans and deep neural networks: towards predicting human color perception in a vast color space
This study systematically compares color representations across self-supervised, supervised, and CLIP-trained deep neural networks using Gromov-Wasserstein Optimal Transport to demonstrate that while early layers of all paradigms align with human perception, only CLIP maintains this structural congruence at the output, enabling reliable predictions of human color perception across vast, previously unexplored color spaces.