TensorFlow implementations of visualization of convolutional neural networks, such as Grad-Class Activation Mapping and guided back propagation
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Updated
Sep 12, 2018 - Python
TensorFlow implementations of visualization of convolutional neural networks, such as Grad-Class Activation Mapping and guided back propagation
Part of the experiments in "A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations"
PyTorch re-implementation of Grad-CAM (+ vanilla/guided backpropagation, deconvnet, and occlusion sensitivity maps)
Use your classification neural network for object detection and localization
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
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++.
pytorch实现Grad-CAM和Grad-CAM++,可以可视化任意分类网络的Class Activation Map (CAM)图,包括自定义的网络;同时也实现了目标检测faster r-cnn和retinanet两个网络的CAM图;欢迎试用、关注并反馈问题...
Public facing deeplift repo
Pytorch implementation of convolutional neural network visualization techniques
visualization:filter、feature map、attention map、image-mask、grad-cam、human keypoint、guided-backpro
📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
Computer vision visualization such as Grad-CAM, etc.
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