  In this paper, we utilize deep visual Representation Learning to address an
important problem in fashion e-commerce: color variants identification, i.e.,
identifying fashion products that match exactly in their design (or style), but
only to differ in their color. At first we attempt to tackle the problem by
obtaining manual annotations (depicting whether two products are color
variants), and train a supervised triplet loss based neural network model to
learn representations of fashion products. However, for large scale real-world
industrial datasets such as addressed in our paper, it is infeasible to obtain
annotations for the entire dataset, while capturing all the difficult corner
cases. Interestingly, we observed that color variants are essentially
manifestations of color jitter based augmentations. Thus, we instead explore
Self-Supervised Learning (SSL) to solve this problem. We observed that existing
state-of-the-art SSL methods perform poor, for our problem. To address this, we
propose a novel SSL based color variants model that simultaneously focuses on
different parts of an apparel. Quantitative and qualitative evaluation shows
that our method outperforms existing SSL methods, and at times, the supervised
model.
