In this article, we present our approach to single-modality vision
representation learning. Understanding vision representations of product
content is vital for recommendations, search, and advertising applications in
e-commerce. We detail and contrast techniques used to fine tune large-scale
vision representation learning models in an efficient manner under low-resource
settings, including several pretrained backbone architectures, both in the
convolutional neural network as well as the vision transformer family. We
highlight the challenges for e-commerce applications at-scale and highlight the
efforts to more efficiently train, evaluate, and serve visual representations.
We present ablation studies for several downstream tasks, including our
visually similar ad recommendations. We evaluate the offline performance of the
derived visual representations in downstream tasks. To this end, we present a
novel text-to-image generative offline evaluation method for visually similar
recommendation systems. Finally, we include online results from deployed
machine learning systems in production at Etsy.

本文提出了一种单模态视觉表征学习的方法，主要用于电子商务中的产品推荐、搜索和广告应用，包括预训练骨干架构、卷积神经网络和视觉变换器家族等。通过离线和在线的方式，我们对实验方法进行了评估和分析，并提出了新的文本到图像生成离线评估方法来评估视觉相似度的推荐系统，在 Etsy 的生产环境中进行了机器学习系统的应用。