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Learning Deep Features for Discriminative Localization
Bolei ZhouοΌ Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba Computer Science and Artificial Intelligence Laboratory, MIT
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Aim Method Remove fully-connected (FC) layers
Using global average pooling (GAP) instead of global max pooling Class activation map (CAM)
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pipeline
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Class Activation Map πΉ π = π₯,π¦ π π (π₯,π¦) π π = π π€ π π πΉ π
πΉ π = π₯,π¦ π π (π₯,π¦) π π (π₯,π¦) π π = π π€ π π πΉ π π π = expβ‘( π π ) π expβ‘( π π ) unit k π π΄π’π π‘ππππππ π‘ππππππ {π€ π π΄π’π π‘ππππππ π‘ππππππ } π=1,..,π
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Class Activation Map β― π π = π π€ π π πΉ π = π π€ π π π₯,π¦ π π (π₯,π¦)
π π = π π€ π π πΉ π = π π€ π π π₯,π¦ π π (π₯,π¦) = π π₯,π¦ π€ π π π π (π₯,π¦) = π₯,π¦ π π€ π π π π (π₯,π¦) unit 1 unit n β― π€ 2 π π (π₯,π¦)= π π€ π π π π (π₯,π¦) π€ π π€ 1 Class Activation Map for class c Class Activation Map for class c
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Experiment CNN types: AlexNet VGGnet GoogLeNet
Network in Network (NIN) Dataset: ILSVRC 2014
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Classification
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Localization
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Deep Features for Generic Localization
CNN types: AlexNet GoogLeNet GoogLeNet-GAP Dataset: SUN397 MIT Indoor67 Scene15 SUN Attribute Caltech101 Caltech256 Stanford Action40 UIUC Event8
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Deep Features for Generic Localization
CUB200 contains 11,788 images, with 5,994 images for training and 5,794 for test. (200 birds species)
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Deep Features for Generic Localization
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Deep Features for Generic Localization
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Visualizing Class-Speciο¬c Units
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πΉ π
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