SmoothGrad implementation in PyTorch

Last update: Jan 05, 2023

Overview

SmoothGrad implementation in PyTorch

PyTorch implementation of SmoothGrad: removing noise by adding noise.

Vanilla Gradients	SmoothGrad	Guided backpropagation	Guided SmoothGrad

And other techniques such as the following are implemented.

Download

git clone https://github.com/pkdn/pytorch-smoothgrad

Usage

Save the saliency maps using VanillaGrad, GuidedBackpropGrad, SmoothGrad, GuidedBackpropSmoothGrad.

python saliency.py --img <image-path>

Save the Grad-CAM image.

python grad_cam.py --img <image-path>

If you do not specify an image path, read the raccoon's data (scipy.misc.face()).

Requirements

PyTorch
torch-vision
numpy
scipy
OpenCV

Environment under Python 3.5.2 is tested.

Acknowledgments

This code is insipired by pytorch-grad-cam.

Owner

SSKH

GitHub Repository

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SmoothGrad implementation in PyTorch

Related tags

Overview

SmoothGrad implementation in PyTorch

Download

Usage

Requirements

Acknowledgments

Owner

SSKH

TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction

AutoPentest-DRL: Automated Penetration Testing Using Deep Reinforcement Learning

IA for recognising Traffic Signs using Keras [Tensorflow]

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[ICCV 2021] Official Pytorch implementation for Discriminative Region-based Multi-Label Zero-Shot Learning SOTA results on NUS-WIDE and OpenImages

Deep Learning (with PyTorch)

In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard test set accuracy

Build upon neural radiance fields to create a scene-specific implicit 3D semantic representation, Semantic-NeRF

(Arxiv 2021) NeRF--: Neural Radiance Fields Without Known Camera Parameters

This is the official implementation for "Do Transformers Really Perform Bad for Graph Representation?".

Datasets, Transforms and Models specific to Computer Vision

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PyTorch inference for "Progressive Growing of GANs" with CelebA snapshot

Simple Pixelbot for Diablo 2 Resurrected written in python and opencv.

Reference implementation of code generation projects from Facebook AI Research. General toolkit to apply machine learning to code, from dataset creation to model training and evaluation. Comes with pretrained models.

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Next-gen Rowhammer fuzzer that uses non-uniform, frequency-based patterns.

🐦 Quickly annotate data from the comfort of your Jupyter notebook

This is the code for HOI Transformer

Official Pytorch Implementation of 'Learning Action Completeness from Points for Weakly-supervised Temporal Action Localization' (ICCV-21 Oral)