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Research map / Detection and segmentation / CNN features

Learning Deep Features for Discriminative Localization

CVPR 2016 · Detection and segmentation › CNN features · approach: Local features and context in CNNs · 11,042 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use global average pooling instead of global max pooling (ablation)GoogLeNet localization · ILSVRC 2014 valtop-5 localization error: 45.26 -> 43.00Helped
Use global average pooling instead of global max pooling (ablation)GoogLeNet classification · ILSVRC 2014 valtop-5 classification error: 13.9 -> 13.2No clear effect
Use CAM instead of backpropagation for localizationGoogLeNet · ILSVRC 2014 valtop-5 localization error: 50.55 -> 43.00Helped
Use higher mapping resolution with the GAP architectureGoogLeNet localization · ILSVRC 2014 valtop-5 localization error: 49.34 -> 43.00Helped
Use multiple heuristic bounding boxes from predicted classes (ablation)GoogLeNet-GAP · ILSVRC 2014 testtop-5 localization error: 42.9 -> 37.1Helped
Extract features from CAM-localized crops instead of full images (ablation)GoogLeNet-GAP · CUB-200-2011accuracy: 63.0% -> 67.8%Helped
Use annotated bounding-box crops instead of full imagesGoogLeNet-GAP · CUB-200-2011accuracy: 63.0% -> 70.5%Helped
Replace the original AlexNet head with GAP (ablation)AlexNet · ILSVRC 2014 valtop-5 classification error: 19.5 -> 26.3Hurt
Add two convolutional layers before GAP (ablation)AlexNet-GAP · ILSVRC 2014 valtop-5 classification error: 26.3 -> 20.9Helped

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