Fashion CUT: Unsupervised domain adaptation for visual pattern
  classification in clothes using synthetic data and pseudo-labels
Enric Moreu Alex Martinelli Martina Naughton Philip Kelly Noel E. O'Connor
Accurate product information is critical for e-commerce stores to allow
customers to browse, filter, and search for products. Product data quality is
affected by missing or incorrect information resulting in poor customer
experience. While machine learning can be used to correct inaccurate or missing
information, achieving high performance on fashion image classification tasks
requires large amounts of annotated data, but it is expensive to generate due
to labeling costs. One solution can be to generate synthetic data which
requires no manual labeling. However, training a model with a dataset of solely
synthetic images can lead to poor generalization when performing inference on
real-world data because of the domain shift. We introduce a new unsupervised
domain adaptation technique that converts images from the synthetic domain into
the real-world domain. Our approach combines a generative neural network and a
classifier that are jointly trained to produce realistic images while
preserving the synthetic label information. We found that using real-world
pseudo-labels during training helps the classifier to generalize in the
real-world domain, reducing the synthetic bias. We successfully train a visual
pattern classification model in the fashion domain without real-world
annotations. Experiments show that our method outperforms other unsupervised
domain adaptation algorithms.