656 citations · 3k across the 29 of their papers we have counts for
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cs.CV2016★ 71 cited
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
Konstantinos Bousmalis, Nathan Silberman, David Dohan +2
Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks. One appealing alternative is rendering synthetic dat…
cs.CV2016★ 588 cited
Domain Separation Networks
Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman +2
The cost of large scale data collection and annotation often makes the application of machine learning algorithms to new tasks or datasets prohibitively expensive. One approach cir…