  We present the 2017 Visual Domain Adaptation (VisDA) dataset and challenge, a
large-scale testbed for unsupervised domain adaptation across visual domains.
Unsupervised domain adaptation aims to solve the real-world problem of domain
shift, where machine learning models trained on one domain must be transferred
and adapted to a novel visual domain without additional supervision. The
VisDA2017 challenge is focused on the simulation-to-reality shift and has two
associated tasks: image classification and image segmentation. The goal in both
tracks is to first train a model on simulated, synthetic data in the source
domain and then adapt it to perform well on real image data in the unlabeled
test domain. Our dataset is the largest one to date for cross-domain object
classification, with over 280K images across 12 categories in the combined
training, validation and testing domains. The image segmentation dataset is
also large-scale with over 30K images across 18 categories in the three
domains. We compare VisDA to existing cross-domain adaptation datasets and
provide a baseline performance analysis using various domain adaptation models
that are currently popular in the field.
