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Regularizing Deep Networks by Modeling and Predicting Label Structure

CVPR 2018 · Label-efficient and robust learning › Recognition and long-tail · approach: Loss design and classifier theory · 33 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add a learned label-decoder auxiliary regularizerVGG-16 · PASCAL 2012 val, no pretraining, no augmentationmIoU: 37.3 -> 42.4Helped
Add a learned label-decoder auxiliary regularizerDenseNet-67 · PASCAL 2012 val, no pretraining, no augmentationmIoU: 40.5 -> 45.2Helped
Add a learned label-decoder auxiliary regularizerDenseNet-67 · PASCAL 2012 val, no pretraining, with augmentationmIoU: 58.8 -> 60.6Helped
Add a learned label-decoder auxiliary regularizerVGG-16 · PASCAL 2012 val, ImageNet pretraining, no augmentationmIoU: 67.1 -> 68.8Helped
Add a learned label-decoder auxiliary regularizerDenseNet-121 · PASCAL 2012 val, ImageNet pretraining, with augmentationmIoU: 71.6 -> 71.9Helped
Add a learned label-decoder auxiliary regularizerResNet-101 · PASCAL 2012 val, ImageNet pretraining, with augmentationmIoU: 75.4 -> 75.9Helped
Add a learned label-decoder auxiliary regularizerDenseNet-67 · PASCAL 2012 val, COCO pretraining, with augmentationmIoU: 72.3 -> 73.6Helped
Use encoder-feature prediction instead of the decoder pathway (ablation)VGG-16 · PASCAL 2012 val, no pretraining, no augmentationmIoU: 37.3 -> 41.1Helped
Use encoder-feature prediction instead of the decoder pathway (ablation)DenseNet-67 · PASCAL 2012 val, no pretraining, no augmentationmIoU: 40.5 -> 44.0Helped
Replace the 32-channel bottleneck with a 128-channel bottleneck (ablation)DenseNet-67 with augmentation, no pretraining · PASCAL 2012 valmIoU: 59.4 -> 60.6Helped
Replace the 128-channel bottleneck with a 256-channel bottleneck (ablation)DenseNet-67 with augmentation, no pretraining · PASCAL 2012 valmIoU: 60.6 -> 59.8Hurt
Unfreeze the decoder parameters during CNN training (ablation)DenseNet-67 + Decoder (128 channel) · PASCAL 2012 val, no pretraining, with augmentationmIoU: 60.6 -> 60.2Hurt

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