  In this paper, we present a method to learn a visual representation adapted
for e-commerce products. Based on weakly supervised learning, our model learns
from noisy datasets crawled on e-commerce website catalogs and does not require
any manual labeling. We show that our representation can be used for downward
classification tasks over clothing categories with different levels of
granularity. We also demonstrate that the learnt representation is suitable for
image retrieval. We achieve nearly state-of-art results on the DeepFashion
In-Shop Clothes Retrieval and Categories Attributes Prediction tasks, without
using the provided training set.
