Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
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Updated
Apr 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
Tensorflow tutorial for various Deep Neural Network visualization techniques
A Simple pytorch implementation of GradCAM and GradCAM++
Can we use explanations to improve hate speech models? Our paper accepted at AAAI 2021 tries to explore that question.
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
Protein-compound affinity prediction through unified RNN-CNN
A repository for explaining feature attributions and feature interactions in deep neural networks.
Pytorch Implementation of recent visual attribution methods for model interpretability
Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers
Pytorch implementation of various neural network interpretability methods
PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)
Implementation of the paper "Shapley Explanation Networks"
ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021
Quantitative Testing with Concept Activation Vectors in PyTorch
CausaLM: Causal Model Explanation Through Counterfactual Language Models
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
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