Unofficial Pytorch implementation of guided backpropagation
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Updated
Mar 17, 2024 - Jupyter Notebook
Unofficial Pytorch implementation of guided backpropagation
AI for COVID-19
CAM, Grad-CAM, Grad-CAM++ and Guided Backpropagation post-hoc explanation methods
Computer vision visualization such as Grad-CAM, etc.
Implementation of Guided GradCAM in Keras
Suite of methods that create attribution maps from image classification models.
Part of the experiments in "A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations"
This repository contains all my theroy reports, written assignments and programming code that I wrote/referrd for the DL course at IIT,Madras taught my advisor Prof.Mitesh Khapra.
Neural network visualization tool after an optional model compression with parameter pruning: (integrated) gradients, guided/visual backpropagation, activation maps for the cao model on the IndianPines dataset
Use your classification neural network for object detection and localization
PyTorch implementation of 'Vanilla' Gradient, Grad-CAM, Guided backprop, Integrated Gradients and their SmoothGrad variants.
Implementation of GradCAM & Guided GradCAM with Tensorflow 2.x
Filter visualization, Feature map visualization, Guided Backprop, GradCAM, Guided-GradCAM, Deep Dream
In this part, I've introduced and experimented with ways to interpret and evaluate models in the field of image. (Pytorch)
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++.
visualization:filter、feature map、attention map、image-mask、grad-cam、human keypoint、guided-backpro
Pytorch implementation of various neural network interpretability methods
TensorFlow implementations of visualization of convolutional neural networks, such as Grad-Class Activation Mapping and guided back propagation
📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
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