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, Finer-CAM, LeGrad, RefineCAM)

Public facing deeplift repo

A Simple pytorch implementation of GradCAM and GradCAM++

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

Tensorflow tutorial for various Deep Neural Network visualization techniques

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

[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

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

PyTorch Explain: Interpretable Deep Learning in Python.

Protein-compound affinity prediction through unified RNN-CNN

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

Pytorch Implementation of recent visual attribution methods for model interpretability

Tools for training explainable models using attribution priors.

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 various neural network interpretability methods

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

All about explainable AI, algorithmic fairness and more

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

Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers