Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
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Updated
Aug 29, 2024 - Python
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++
Tensorflow tutorial for various Deep Neural Network visualization techniques
A curated list of trustworthy deep learning papers. Daily updating...
[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 Implementation of recent visual attribution methods for model interpretability
Protein-compound affinity prediction through unified RNN-CNN
PyTorch Explain: Interpretable Deep Learning in Python.
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
Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers
[ICCV 2021] Towards Interpretable Deep Metric Learning with Structural Matching
ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021
Implementation of the paper "Shapley Explanation Networks"
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