Repository for the Explainable Deep One-Class Classification paper
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Updated
Aug 30, 2023 - Python
Repository for the Explainable Deep One-Class Classification paper
A utility for generating heatmaps of YOLOv8 using Layerwise Relevance Propagation (LRP/CRP).
This repository introduces different Explainable AI approaches and demonstrates how they can be implemented with PyTorch and torchvision. Used approaches are Class Activation Mappings, LIMA and SHapley Additive exPlanations.
AI explainability is a big topic in the tech world right now, & experts have been working to create ways for machines to start explaining what they are doing. So I am compiling all great examples of tools used to help with AI explainability.
Sensor selection for process monitoring based on deciphering acoustic emissions from different dynamics of the Laser Powder Bed Fusion process using Empirical Mode Decompositions and Interpretable Machine Learning
📺 A Python library for pruning and visualizing Keras Neural Networks' structure and weights
Pneumonia Detection Software with Database
This repository contains some work on Explainable Artificial Intelligence
GEF - Generative Explanation Framework was introduced by Liu et al., which generates textual explanations for a text classification task. The talk was given as part of a seminar at the Saarland University.
Speech reconstruction from pre-trained CNN embeddings
This repository contains my personal notes and Jupyter notebooks on Deep Learning Specialization course at the university Haute-Alsace.
One of the firsts dataset level explanability libraries for 1d signal using GRAD-CAM++
Explainable deep networks that are not only as accurate as their black-box deep-learning counterparts but also as interpretable as state-of-the-art explanation techniques.
A curated list of awesome contrastive explanation in ML resources
Graduate research project in computer vision and deep learning explainability
Paper and resources collections about interpretable AI (XAI)
A curated list of papers on explainability and interpretability of self-driving models
In Search of Probeable Generalization Measures [ICMLA2021]
T-EBAnO: Explaining deep learning black-box models for Natural Language Processing.
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