Grad-CAM for AlexNet to explain the reason of classification

버전 1.0.1 (8.17 MB) 작성자: Takuji Fukumoto
Grad-CAM for visual explanation with re-trained AlexNet

다운로드 수: 283

업데이트 날짜: 2020/12/25

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Class Activation Mapping(CAM) is a good method to explain why the model classify the object as that.
https://jp.mathworks.com/matlabcentral/fileexchange/69357-class-activation-mapping
But network models which can be applied for CAM are limited.
Grad-CAM is the method to generalize CAM to work with many kinds of networks.

Through this demo, you can learn workflow from retraining model(AlexNet) to applying Grad-CAM on it.

[Japanese]
CNNを用いたディープラーニングによる分類の判定精度は非常に高く、多くの領域での画像自動判定に利用されています。一方で、内部がブラックボックスで「なぜその判定になったのかわからない」点に不安を感じる方もいます。Class Activation Mapping(CAM)は判定要因の可視化に非常に便利ですが、適用できるネットワークに制限があります。

Grad-CAMはGradietを利用して任意のネットワーク・層でCAMを一般化した方法です。
このサンプルでAlexNetでの転移学習からGrad-CAMの適用までのコードを確認できます。

[Keyword]
画像処理・IPCVデモ・ディープラーニング・深層学習・転移学習・入門・物体認識・画像分類・コンピュータビジョン・ニューラルネットワーク・人工知能・外観検査・可視化

Paper:
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization.
Ramprasaath R. Selvaraju, etc
https://arxiv.org/abs/1610.02391

인용 양식

Takuji Fukumoto (2022). Grad-CAM for AlexNet to explain the reason of classification (https://github.com/mathworks/Grad-CAM-for-AlexNet-to-explain-the-reason-of-classification/releases/tag/1.0.1), GitHub. 검색됨 .

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