A Simple pytorch implementation of GradCAM and GradCAM++
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Updated
Apr 23, 2019 - Jupyter Notebook
A Simple pytorch implementation of GradCAM and GradCAM++
GradCAM++ and GradCAM for Fastai_v1.0
Keras implementation of GradCam & GradCam++ to Dogs vs. Cats classification model
Gcam is an easy to use Pytorch library that makes model predictions more interpretable for humans. It allows the generation of attention maps with multiple methods like Guided Backpropagation, Grad-Cam, Guided Grad-Cam and Grad-Cam++.
Analysis of visualization methods for relevant areas within images for a trained CNN. Used the GTSRB dataset as well as Activation Maximization, Saliency Map, GradCam, and Gradcam++ methods.
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tensorflow.keras implementation of gradcam and gradcam++
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Frame-agnostic XAI Library for Computer Vision, for understanding why models behave that way.
One of the firsts dataset level explanability libraries for 1d signal using GRAD-CAM++
One of the first implementations of Grad-CAM ++ for time series / 1d signal.
Neural network visualization toolkit for tf.keras
Explainable AI for Image Classification
A tool for classifying an image into a disaster type, utilizing Python
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)
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