  Color constancy is our ability to perceive constant colors across varying
illuminations. Here, we trained deep neural networks to be color constant and
evaluated their performance with varying cues. Inputs to the networks consisted
of the cone excitations in 3D-rendered images of 2115 different 3D-shapes, with
spectral reflectances of 1600 different Munsell chips, illuminated under 278
different natural illuminations. The models were trained to classify the
reflectance of the objects. One network, Deep65, was trained under a fixed
daylight D65 illumination, while DeepCC was trained under varying
illuminations. Testing was done with 4 new illuminations with equally spaced
CIEL*a*b* chromaticities, 2 along the daylight locus and 2 orthogonal to it. We
found a high degree of color constancy for DeepCC, and constancy was higher
along the daylight locus. When gradually removing cues from the scene,
constancy decreased. High levels of color constancy were achieved with
different DNN architectures. Both ResNets and classical ConvNets of varying
degrees of complexity performed well. However, DeepCC, a convolutional network,
represented colors along the 3 color dimensions of human color vision, while
ResNets showed a more complex representation.
