  In this paper, we utilize deep visual Representation Learning to address the
problem of identification of color variants. In particular, we address color
variants identification in fashion products, which refers to the problem of
identifying fashion products that match exactly in their design (or style), but
only to differ in their color. Firstly, we solve this problem by obtaining
manual annotations depicting whether two products are color variants. Having
obtained such annotations, we train a triplet loss based neural network model
to learn deep 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. Hence, we rather explore the use
of self-supervised learning to obtain the representations. We observed that
existing state-of-the-art self-supervised methods do not perform competitive
against the supervised version of our color variants model. To address this, we
additionally propose a novel contrastive loss based self-supervised color
variants model. Intuitively, our model focuses on different parts of an object
in a fixed manner, rather than focusing on random crops typically used for data
augmentation in existing methods. We evaluate our method both quantitatively
and qualitatively to show that it outperforms existing self-supervised methods,
and at times, the supervised model as well.
