tensorflow implementation of Grad-CAM (CNN visualization)
The main features of insikk/grad-cam-tensorflow are: Explainable AI Libraries, Model Optimization.
Open-source alternatives to insikk/grad-cam-tensorflow include: jacobgil/pytorch-grad-cam — This project is a computer vision explainable AI library and framework for PyTorch, providing a suite of tools to… morvanzhou/tensorflow-tutorial — This project is a collection of educational resources and reference implementations for neural network development… algofairness/blackboxauditing — This repository contains a sample implementation of Gradient Feature Auditing (GFA) meant to be generalizable to most… andosa/treeinterpreter — TreeInterpreter. ankurtaly/integrated-gradients — (a.k.a. Path-Integrated Gradients, a.k.a. Axiomatic Attribution for Deep Networks). alvinwan/neural-backed-decision-trees — Project Page // Paper // No-code Web Demo // Colab Notebook.
This project is a computer vision explainable AI library and framework for PyTorch, providing a suite of tools to visualize and audit the internal decision-making processes of deep neural networks. It serves as a neural network attribution tool and debugging utility to identify which image regions drive model predictions. The library is distinguished by its support for both gradient-based and gradient-free attribution methods, allowing for the generation of visual heatmaps and attribution maps without requiring modifications to the original model source code. It further differentiates itself
This project is a collection of educational resources and reference implementations for neural network development using TensorFlow. It serves as a comprehensive learning course, machine learning curriculum, and practical implementation guide for building deep learning architectures. The codebase provides instructional materials and examples covering a wide range of model types, including convolutional neural networks for image classification, recurrent networks and long short-term memory cells for sequential data, and autoencoders for generative modeling. It also includes implementations for
This repository contains a sample implementation of Gradient Feature Auditing (GFA) meant to be generalizable to most datasets. For more information on the repair process, see our paper on Certifying and Removing Disparate Impact. For information on the full auditing process, see our paper on…
Project Page // Paper // No-code Web Demo // Colab Notebook