588 citations · 699 across the 4 of their papers we have counts for
4 papers
Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion
Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan +2
The predictive performance of supervised learning algorithms depends on the quality of labels. In a typical label collection process, multiple annotators provide subjective noisy e…
The Devil is in the Decoder: Classification, Regression and GANs
Zbigniew Wojna, Vittorio Ferrari, Sergio Guadarrama +4
Many machine vision applications, such as semantic segmentation and depth prediction, require predictions for every pixel of the input image. Models for such problems usually consi…
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…
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…