  In this paper, we present CLCC, a novel contrastive learning framework for
color constancy. Contrastive learning has been applied for learning
high-quality visual representations for image classification. One key aspect to
yield useful representations for image classification is to design illuminant
invariant augmentations. However, the illuminant invariant assumption conflicts
with the nature of the color constancy task, which aims to estimate the
illuminant given a raw image. Therefore, we construct effective contrastive
pairs for learning better illuminant-dependent features via a novel raw-domain
color augmentation. On the NUS-8 dataset, our method provides $17.5\%$ relative
improvements over a strong baseline, reaching state-of-the-art performance
without increasing model complexity. Furthermore, our method achieves
competitive performance on the Gehler dataset with $3\times$ fewer parameters
compared to top-ranking deep learning methods. More importantly, we show that
our model is more robust to different scenes under close proximity of
illuminants, significantly reducing $28.7\%$ worst-case error in data-sparse
regions.
