Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks

• URL: http://arxiv.org/abs/2210.09919v1
• Date: Tue, 18 Oct 2022 15:02:51 GMT
• Title: Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks
• Authors: Miquel Mart\'i i Rabad\'an, Alessandro Pieropan, Hossein Azizpour and Atsuto Maki
• Abstract summary: We propose Dense FixMatch, a simple method for online semi-supervised learning of dense and structured prediction tasks. We enable the application of FixMatch in semi-supervised learning problems beyond image classification by adding a matching operation on the pseudo-labels. Dense FixMatch significantly improves results compared to supervised learning using only labeled data, approaching its performance with 1/4 of the labeled samples.
• Score: 68.36996813591425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose 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. We enable the application of FixMatch in semi-supervised learning problems 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 evaluate it on semi-supervised semantic segmentation on Cityscapes and Pascal VOC with different percentages of labeled data and ablate 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.

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