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The Origin of Grad-CAM

The Origin of Grad-CAM

This is the slide of EAGLYS AI Weekly Study Meeting.

Today's presentation is about the most famous method in XAI, Grad-CAM. Most people have ever heard of the name, however there's few people to know about the origin and idea. I explained this point very carefully.

I hope you get some knowledge of Grad-CAM. Thanks.

吉田 慎太郎 @微分

October 25, 2020

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  1. The Origin of Grad-CAM AI Study Meeting #4 @Eaglys on

    2020/10/25 Shintaro Yoshida @sht_47
  2. The Features of Grad-CAM • Grad-CAM(Gradient-weighted Class Activation Mapping, 2016,

    Ramprasaath) ◦ Most Famous Method in XAI ( I described the reason in later slide) ◦ Update CAM(2015, Zhou) 、Generalize to Any Kind of CNN Architecture • The Goal of XAI(Explainable Artificial Intelligence) Identify the Mode of Failure (AI << Human) Predict with more Confidence (AI ≒ Human) AI teaches Human (AI >> Human)
  3. The Content - The referred Paper of Grad-CAM - -

    - Grad-CAMのモデル中身 - Result and Discussion - Implement with Pytorch and Google Colaboratory
  4. NIN(Network In Network, 2014 Lin et al) - Proficient Paper

    because of two great ideas Introduce 1x1 Conv to reduce the calculation cost ( Applied to InceptionNet、ResNet Botttleneck Block) Introduce GAP(Global Average Pooling) → Recently Adaptive Average Pooling is used • GAP Performed as a Structural Regularizer ◦ More Native to the correspondence between Feature Map and Category ◦ NO Added Parameter ◦ Robust to Spatial Translation
  5. Object Detectors Emerge In Deep Scene Cnns(2015 Zhou et al)

    - CNN Model Scene Recognition → Object Detector Emerges No Supervised Dataset of Object Classification and Detection In Previous Research, Object Classification → Object Localization Places Database (2014 Zhou et al )
  6. CAM(Class Activation Mapping 2015 Zhou et al) … … Final

    Conv GAP FC K Featuer Maps K Element … C class a a 1 Generate CAM Using
  7. CAM(Class Activation Mapping) … … Final Conv GAP FC 4096

    Feature Maps 4096 Element … 1000 Class VGG16 (ImageNet) 7 7
  8. Math Equation and Concept of CAM Sum with i, j

    Weighted Sum with k Each Process is Independent Z is size of Feature Map (Z=49)
  9. Usage of CAM( After Inference) Average With i, j (Image

    Source : Zhou et al 2015) CAM Weighted Sum with k Inference Generate CAM Weighted Sum with k
  10. Guided Back-Propagation(2015 Springenberg) - Deconvolutional Network (2011 Zeiler) Opposite Process

    of Max Pooling - Guided Backprop Combine with DeconvNet and ReLU BackPropagation
  11. Result of Guided-Backprop Batch Size : 64 Learning Rate :

    0.01 Weight Decay : 0.001 Optimizer : SGD Conv6 Conv9
  12. Grad-CAM(2016 Ramprasaath) CAM limits with GAP → Grad-CAM generalize to

    Any Architecture Combine CAM(Corase) with Guided-Backprop(Fined-Grained) Insert ReLU to CAM(Only Positive Value is enough) No need to Architectural Change and Re-Train Sum with i and j Weighted Sum with Weighted Sum with
  13. Result 1 of Grad-CAM - Microsoft COCO Dataset - Sample

    from Validation Dataset - Mistake with Ice Cream
  14. Result 2 of Grad-CAM Mistake at VGG@ImageNet Whether the model

    has bias or not
  15. Implement - Pytorch 1.6 https://github.com/sht47/grad-cam-Pytorch1.6 - Tensorflow 2.3 (Under Construction)