How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, Rory Sayres
The main features of tensorflow/tcav are: Deep Learning Frameworks, Explainable AI Libraries.
Projects with overlapping indexed features include: tensorflow/lucid — Lucid is a TensorFlow interpretability toolkit and visualization library designed to analyze the internal… alankbi/detecto — Build fully-functioning computer vision models with PyTorch. albermax/innvestigate — A toolbox to iNNvestigate neural networks' predictions! albu/albumentations — Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for… algofairness/blackboxauditing — This repository contains a sample implementation of Gradient Feature Auditing (GFA) meant to be generalizable to most… aerdem4/lofo-importance — Leave One Feature Out Importance.
Lucid is a TensorFlow interpretability toolkit and visualization library designed to analyze the internal representations of neural networks. It functions as a gradient-based optimization framework that generates images and atlases to reveal the features learned by specific neurons and layers. The library enables the creation of activation atlases and the mapping of high-dimensional neural activations into lower-dimensional spaces to study model behavior. It utilizes differentiable image parametrization to optimize visual inputs that maximally activate network components. The system covers a
A toolbox to iNNvestigate neural networks' predictions!
