CAD Models to Real-World Images: A Practical Approach to Unsupervised
  Domain Adaptation in Industrial Object Classification
Dennis Ritter Mike Hemberger Marc Hönig Volker Stopp Erik Rodner Kristian Hildebrand
In this paper, we systematically analyze unsupervised domain adaptation
pipelines for object classification in a challenging industrial setting. In
contrast to standard natural object benchmarks existing in the field, our
results highlight the most important design choices when only category-labeled
CAD models are available but classification needs to be done with real-world
images. Our domain adaptation pipeline achieves SoTA performance on the VisDA
benchmark, but more importantly, drastically improves recognition performance
on our new open industrial dataset comprised of 102 mechanical parts. We
conclude with a set of guidelines that are relevant for practitioners needing
to apply state-of-the-art unsupervised domain adaptation in practice. Our code
is available at https://github.com/dritter-bht/synthnet-transfer-learning.