Comments (2)
Oh, now I remember. Because the guided grad was proposed in all-conv paper which only has conv layers, I decided to modify the relus at features (i.e., conv side) and not classifier (i.e., linear side). You can change it and see how it performs if all relus are modified.
You can read the original paper and this blog for more details.
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Hey,
Sorry for the late reply, I believe it is like that because I had the impression from the paper that only the signals at feature level (convs) are imputed (See Fig1 from the paper). But quickly skimming the paper again, I fear you might be right about the activations at the classifier. I will have a detailed look when I have time.
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Related Issues (20)
- Why this cam-zoo don't have grad-cam++?May you add grad-cam++ in this project? HOT 1
- Support for LayerCAM HOT 4
- a question on "cam = np.ones(target.shape[1:], dtype=np.float32)" in gradcam.py HOT 2
- How to get the sampling points with deformable conv? HOT 1
- A question about the method to get output from specific layer HOT 4
- Support for non-VGG models. HOT 1
- question on image generation
- Image Reconstruction size is same as conv1 layer HOT 1
- Visualizations for CNN trained on timeseries classification HOT 1
- Could you please provide the feature importance included dataset that has been generated? HOT 1
- AttributeError: 'MyCNN' object has no attribute 'features' HOT 1
- Extract gradient without model_output HOT 3
- GradCAM
- GradCAM HOT 1
- Why take np.maximum(cam, 0) in GradCam? HOT 1
- attention HOT 1
- Help with understanding layer backpropagation. HOT 1
- Can this tool be used on non-classification tasks HOT 1
- Code application related issues HOT 1
- RUN
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