#Interpretable-deep-learning
Showing 11 of 11 repositories tagged #interpretable-deep-learning, ranked by stars
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)
A curated list of trustworthy deep learning papers. Continually updating...
Tensorflow tutorial for various Deep Neural Network visualization techniques
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
Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers
Implementation of the paper "Shapley Explanation Networks"
[ICLR 23 spotlight] An automatic and efficient tool to describe functionalities of individual neurons in DNNs
Multislice PHATE for tensor embeddings
[CVPR 2025] Concept Bottleneck Autoencoder (CB-AE) -- efficiently transform any pretrained (black-box) image generative model into an interpretable generative concept bottleneck model (CBM) with minimal concept supervision, while preserving image quality