Efficiently learning visual representations of items is vital for large-scale recommendations. In this article we compare several pretrained efficient backbone architectures, both in the convolutional neural network (CNN) and in the vision transformer (ViT) family. We describe challenges in e-commerce vision applications at scale and highlight methods to efficiently train, evaluate, and serve visual representations. We present ablation studies evaluating visual representations in several downstream tasks. To this end, we present a novel multilingual text-to-image generative offline evaluation method for visually similar recommendation systems. Finally, we include online results from deployed machine learning systems in production on a large scale e-commerce platform.
@article{arxiv.2305.13399,
title = {Efficient Large-Scale Visual Representation Learning And Evaluation},
author = {Eden Dolev and Alaa Awad and Denisa Roberts and Zahra Ebrahimzadeh and Marcin Mejran and Vaibhav Malpani and Mahir Yavuz},
journal= {arXiv preprint arXiv:2305.13399},
year = {2023}
}