saliency
Emotion_Detection_CNN_keras
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| saliency | Emotion_Detection_CNN_keras | |
|---|---|---|
| 4 | 1 | |
| 929 | 13 | |
| 0.8% | - | |
| 3.6 | 0.0 | |
| about 1 month ago | over 2 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | - |
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saliency
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[D] Is the math in Integrated gradients (4K citations) wrong?
Found relevant code at https://github.com/PAIR-code/saliency + all code implementations here
- How to display which parts of a single image a Keras model found to be the most significant when making a prediction?
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Gradients of model output layer and intermediate layer wrt inputs
I’m trying to visualize model layer outputs using the saliency core package package on a simple conv net. This requires me to compute the gradients of the model output layer and intermediate convolutional layer output w.r.t the input. I’ve attempted to do this in the last code block, but I run into the error
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A Visual History of Interpretation for Image Recognition
[2]: https://github.com/PAIR-code/saliency
Emotion_Detection_CNN_keras
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Emotion Detection CNN using keras
View on GitHub
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