We discuss Dense FixMatch, a simple method for online semi-supervised learning of dense and structured prediction tasks combining pseudo-labeling and consistency regularization via strong data augmentation. It is an application of FixMatch enabled beyond image classification by adding a matching operation on the pseudo-labels. This allows us to still use the full strength of data augmentation pipelines, including geometric transformations.
We evaluated it on semi-supervised semantic segmentation on Cityscapes and Pascal VOC with different percentages of labeled data, and ablated design choices and hyper-parameters. Dense FixMatch significantly improves results compared to supervised learning using only labeled data, approaching its performance with 1/4 of the labeled samples.
[1] Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks [link]
[2] An analysis of over-sampling labeled data in semi-supervised learning with FixMatch [link]
Bio
Atsuto Maki is a Professor of Computer Science at KTH Royal Institute of Technology, Sweden. He obtained BEng and MEng in electrical engineering from Kyoto University and the University of Tokyo, respectively, and his PhD degree in computer science from KTH. Previously he was an associate professor at the Graduate School of Informatics, Kyoto University, and then a senior researcher at Toshiba’s Cambridge Research Lab in the UK. His research interests cover a broad range of topics in machine learning, deep learning, and computer vision, including motion and object recognition, clustering, subspace analysis, stereopsis, and representation learning. He has been serving as a program committee member at major computer vision conferences, e.g. as an area chair of ICCV and ECCV.