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Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks
Miquel Martí i Rabadán Alessandro Pieropan Hossein Azizpour Atsuto Maki
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.
Code Repositories
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| semi-supervised-semantic-segmentation-on-15 | Dense FixMatch (DeepLabv3+ ResNet-101, over-sampling, single pass eval) | Validation mIoU: 74.73% |
| semi-supervised-semantic-segmentation-on-15 | Dense FixMatch (DeepLabv3+ ResNet-50, over-sampling, single pass eval) | Validation mIoU: 71.69% |
| semi-supervised-semantic-segmentation-on-2 | Dense FixMatch (DeepLabv3+ ResNet-101, uniform sampling, single pass eval) | Validation mIoU: 73.91% |
| semi-supervised-semantic-segmentation-on-2 | Dense FixMatch (DeepLabv3+ ResNet-50, uniform sampling, single pass eval) | Validation mIoU: 73.39% |
| semi-supervised-semantic-segmentation-on-21 | Dense FixMatch (DeepLabv3+ ResNet-50, over-sampling, single pass eval) | Validation mIoU: 52.15 |
| semi-supervised-semantic-segmentation-on-21 | Dense FixMatch (DeepLabv3+ ResNet-101, over-sampling, single pass eval) | Validation mIoU: 54.85 |
| semi-supervised-semantic-segmentation-on-22 | Dense FixMatch (DeepLabv3+ ResNet-101, over-sampling, single pass eval) | Validation mIoU: 71.1% |
| semi-supervised-semantic-segmentation-on-22 | Dense FixMatch (DeepLabv3+ ResNet-50, uniform sampling, single pass eval) | Validation mIoU: 70.65% |
| semi-supervised-semantic-segmentation-on-35 | Dense FixMatch (DeepLabv3+ ResNet-101, over-sampling, single pass eval) | Validation mIoU: 66.97 |
| semi-supervised-semantic-segmentation-on-35 | Dense FixMatch (DeepLabv3+ ResNet-50, over-sampling, single-pass eval) | Validation mIoU: 65.81 |
| semi-supervised-semantic-segmentation-on-4 | Dense FixMatch (DeepLabv3+ ResNet-101, over-sampling, single pass eval) | Validation mIoU: 65.82% |
| semi-supervised-semantic-segmentation-on-4 | Dense FixMatch (DeepLabv3+ ResNet-50, over-sampling, single pass eval) | Validation mIoU: 62.49% |
| semi-supervised-semantic-segmentation-on-40 | Dense FixMatch (DeepLabv3+ ResNet-101, over-sampling, single pass eval) | Validation mIoU: 80.82 |
| semi-supervised-semantic-segmentation-on-40 | Dense FixMatch (DeepLabv3+ ResNet-50, over-sampling, single pass eval) | Validation mIoU: 79.98 |
| semi-supervised-semantic-segmentation-on-9 | Dense FixMatch (DeepLabv3+ ResNet-101, over-sampling, single pass eval) | Validation mIoU: 72.04 |
| semi-supervised-semantic-segmentation-on-9 | Dense FixMatch (DeepLabv3+ ResNet-50, over-sampling, single pass eval) | Validation mIoU: 69.02 |
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