text: 402,125,1403140600480747522,147951210,David Berthelot,"New paper: AdaMatch - Unifying Unsupervised Domain Adaptation (UDA) and Semi-Supervised Learning (SSL) and SSDA. Nearly doubles SotA accuracy for UDA on non-pretrained DomainNet. <LINK> 1/3 he technique itself is an extension of FixMatch: the two biggest differences being random logit interpolation and relative confidence thresholding. 2/3 <LINK> There are plenty of tables comparing the effects of applying SSL techniques to UDA, SSDA and UDA techniques such as MCD to SSL and SSDA. The picture I like best is the one that compares convergence between MCD and AdaMatch over time. 3/3 <LINK> Work done with @BeccaRoelofs (co-first author), Kihyuk Sohn, Nicholas Carlini, @alexey2004",https://arxiv.org/abs/2106.04732,"We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one. With the goal of generality, we introduce AdaMatch, a method that unifies the tasks of unsupervised domain adaptation (UDA), semi-supervised learning (SSL), and semi-supervised domain adaptation (SSDA). In an extensive experimental study, we compare its behavior with respective state-of-the-art techniques from SSL, SSDA, and UDA on vision classification tasks. We find AdaMatch either matches or significantly exceeds the state-of-the-art in each case using the same hyper-parameters regardless of the dataset or task. For example, AdaMatch nearly doubles the accuracy compared to that of the prior state-of-the-art on the UDA task for DomainNet and even exceeds the accuracy of the prior state-of-the-art obtained with pre-training by 6.4% when AdaMatch is trained completely from scratch. Furthermore, by providing AdaMatch with just one labeled example per class from the target domain (i.e., the SSDA setting), we increase the target accuracy by an additional 6.1%, and with 5 labeled examples, by 13.6%. ","AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain