Attribution methods that explain image classification models, implemented in PyTorch, and support batch input and GPU.
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Updated
Oct 6, 2024 - Python
Attribution methods that explain image classification models, implemented in PyTorch, and support batch input and GPU.
TeleXGI: Explainable Gastrointestinal Image Classification for TeleSurgery Systems
Implementation for Conditional Text GANs and Analysis with Integrated Gradients
This repository is the code basis for the paper titled "Balancing Privacy and Explainability in Federated Learning"
Reproducible code for our paper "Explainable Learning with Gaussian Processes"
Implementing algorithms based on the analysis of the gradients in NN computational graphs to provide nice insights for Explainable AI
Exercise on interpretability with integrated gradients.
Code and data for the ACL 2023 NLReasoning Workshop paper "Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods" (Feldhus et al., 2023)
SyReNN: Symbolic Representations for Neural Networks
Suite of methods that create attribution maps from image classification models.
The code for integrated gradients in torch.
Scripts to reproduce results within the following manuscript: Perez, I., Skalski, P., Barns-Graham, A., Wong, J. and Sutton, D. (2022) Attribution of Predictive Uncertainties in Classification Models, 38th Conference on Uncertainty in Artificial Intelligence (UAI), Eindhoven, Netherlands, 2022.
simple implementation of Expected Gradients and Integrated Gradients by pytorch
Source code for the IJCKG2021 paper "Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction".
Public facing deeplift repo
Pytorch implementation of various neural network interpretability methods
A small repository to test Captum Explainable AI with a trained Flair transformers-based text classifier.
PyTorch implementation of 'Vanilla' Gradient, Grad-CAM, Guided backprop, Integrated Gradients and their SmoothGrad variants.
Implementation of 2 XAI methods to visualize the region highlighted by the network to make a prediction
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