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48 search results found

Pytorch Grad Cam ⭐ 8,723

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

Diffusion attentive attribution maps for interpreting Stable Diffusion.

Traceml ⭐ 490

Engine for ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.

Quantus ⭐ 460

Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations

Machine_learning_tutorials ⭐ 421

Code, exercises and tutorials of my personal blog ! 📝

Vit Explain ⭐ 260

Explainability for Vision Transformers

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

Explainable Cnn ⭐ 196

📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

Diffusers Interpret ⭐ 143

Diffusers-Interpret 🤗🧨🕵️‍♀️: Model explainability for 🤗 Diffusers. Get explanations for your generated images.

Path_explain ⭐ 134

A repository for explaining feature attributions and feature interactions in deep neural networks.

Hierarchical Dnn Interpretations ⭐ 119

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

Dialogue Understanding ⭐ 82

This repository contains PyTorch implementation for the baseline models from the paper Utterance-level Dialogue Understanding: An Empirical Study

Pytorch_explain ⭐ 80

PyTorch Explain: Logic Explained Networks in Python.

Deep Explanation Penalization ⭐ 74

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

Pytorch implementation of "Explainable and Explicit Visual Reasoning over Scene Graphs "

Information Bottlenecks for Attribution

Interpretability By Parts ⭐ 55

[CVPR 2020 Oral] Interpretable and Accurate Fine-grained Recognition via Region Grouping

Prototree ⭐ 46

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

Torchprism ⭐ 40

Principal Image Sections Mapping. Convolutional Neural Network Visualisation and Explanation Framework

Graphlime ⭐ 35

This is a Pytorch implementation of GraphLIME

Color_distillation ⭐ 33

Codes for CVPR 2020 paper "Learning to Structure an Image with Few Colors". Critical structure for network recognition. #explainable-ai

Demo code for the paper: OccAM's Laser

Ddsm Visual Primitives ⭐ 31

Using deep learning to discover interpretable representations for mammogram classification and explanation

Repository for our NeurIPS 2022 paper "Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off"

Intel Xai Tools ⭐ 23

Explainable AI Tooling (XAI). XAI is used to discover and explain a model's prediction in a way that is interpretable to the user. Relevant information in the dataset, feature-set, and model's algorithms are exposed.

Imle Net ⭐ 20

Official implementation of our IEEE:SMC 2021 paper "IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification"

GitHub repository for DORA: Data-agnOstic Representation Analysis paper. DORA allows to find outlier representations in Deep Neural Networks.

Xai Demonstrator ⭐ 17

The XAI Demonstrator is a modular platform that lets users interact with production-grade Explainable AI (XAI) systems.

Protopformer ⭐ 17

ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition

Gradcam_pytorch ⭐ 16

GradCAM Pytorch

PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)

Explainableml Vision ⭐ 14

This repository introduces different Explainable AI approaches and demonstrates how they can be implemented with PyTorch and torchvision. Used approaches are Class Activation Mappings, LIMA and SHapley Additive exPlanations.

Icml 2023 Route Interpret Repeat ⭐ 12

Official repository of ICML 2023 paper: Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat

Cnn_explainer ⭐ 11

Making CNNs interpretable.

Differential_ig ⭐ 10

Source code for the differential saliency method used in "Re-understanding Finite-State Representations of Recurrent Policy Networks"

SLISEMAP: Combining supervised dimensionality reduction with local explanations

Visual Explanation using Uncertainty based Class Activation Maps

Lime Caae ⭐ 8

Pytorch Implementation of the Explainable Conditional Adversarial Autoencoder using LIME

Frame-agnostic XAI Library for Computer Vision, for understanding why models behave that way.

Explainable Models With Consistent Interpretations ⭐ 8

Official repository for the AAAI-21 paper 'Explainable Models with Consistent Interpretations'

A collection of algorithms of counterfactual explanations.

A toolkit for efficent computation of saliency maps for explainable AI attribution. This tool was developed at Lawrence Livermore National Laboratory.

Counterfactual Explanation Based On Gradual Construction For Deep Networks ⭐ 6

Counterfactual Explanation Based on Gradual Construction for Deep Networks Pytorch

Shapley_valuation ⭐ 6

PyTorch reimplementation of computing Shapley values via Truncated Monte Carlo sampling from "What is your data worth? Equitable Valuation of Data" by Amirata Ghorbani and James Zou [ICML 2019]

Interpretable Explanations Of Black Boxes By Meaningful Perturbation ⭐ 6

Interpretable Explanations of Black Boxes by Meaningful Perturbation Pytorch

Deep Learning Resources ⭐ 5

Curated list of DL Resources [Updated 2019]

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