interpretable-deep-learning

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

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

Public facing deeplift repo

A Simple pytorch implementation of GradCAM and GradCAM++

A curated list of trustworthy deep learning papers. Daily updating...

Tensorflow tutorial for various Deep Neural Network visualization techniques

[ECCV 2020] QAConv: Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting, and [CVPR 2022] GS: Graph Sampling Based Deep Metric Learning

Can we use explanations to improve hate speech models? Our paper accepted at AAAI 2021 tries to explore that question.

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

PyTorch Explain: Interpretable Deep Learning in Python.

Pytorch Implementation of recent visual attribution methods for model interpretability

Protein-compound affinity prediction through unified RNN-CNN

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

Tools for training explainable models using attribution priors.

Pytorch implementation of various neural network interpretability methods

All about explainable AI, algorithmic fairness and more

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

[ICCV 2021] Towards Interpretable Deep Metric Learning with Structural Matching

Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers

[ICLR 23] A new framework to transform any neural networks into an interpretable concept-bottleneck-model (CBM) without needing labeled concept data