florianjac

florianjac's Stars

• HPAI-BSC/Focus-Metric

  Language:Python184

• tensorflow/tcav

  Code for the TCAV ML interpretability project

  Language:Jupyter Notebook634151

• PAIR-code/saliency

  Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).

  Language:Jupyter Notebook962192

• ZhengzeZhou/slime

  Language:JavaScript168

• totti0223/gradcamplusplus

  keras implementation of gradcam_plus_plus

  Language:Jupyter Notebook4216

• samson6460/tf_keras_gradcamplusplus

  tensorflow.keras implementation of gradcam and gradcam++

  Language:Python417

• giorgiovisani/lime_stability

  Language:Jupyter Notebook3312

• jphall663/awesome-machine-learning-interpretability

  A curated list of awesome responsible machine learning resources.

  3.7k591

• shap/shap

  A game theoretic approach to explain the output of any machine learning model.

  Language:Jupyter Notebook23.2k3.3k

• wangyongjie-ntu/Awesome-explainable-AI

  A collection of research materials on explainable AI/ML

  Language:Markdown1.4k199

• keplr-io/quiver

  Interactive convnet features visualization for Keras

  Language:JavaScript1.8k223

• ashishpatel26/Tools-to-Design-or-Visualize-Architecture-of-Neural-Network

  Tools to Design or Visualize Architecture of Neural Network

  4.6k582

• adityac94/Grad_CAM_plus_plus

  A generalized gradient-based CNN visualization technique

  Language:Python28956

• davidbau/dissect

  Code for the Proceedings of the National Academy of Sciences 2020 article, "Understanding the Role of Individual Units in a Deep Neural Network"

  Language:Python30051

• ruthcfong/pytorch-grad-cam

  PyTorch implementation of Grad-CAM

  Language:Python1

• ruthcfong/pytorch-explain-black-box

  PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation

  Language:Jupyter Notebook91

• ruthcfong/perturb_explanations

  Code for Fong and Vedaldi 2017, "Interpretable Explanations of Black Boxes by Meaningful Perturbation"

  Language:Jupyter Notebook305

• coallaoh/WhitenBlackBox

  Towards Reverse-Engineering Black-Box Neural Networks, ICLR'18

  Language:Python5413

• albermax/interpretable_ai_book__sw_chapter

  The code snippets for the SW chapter of the "Interpretable AI" book.

  Language:Jupyter Notebook176

• GuyHacohen/curriculum_learning

  Code implementing the experiments described in the paper "On The Power of Curriculum Learning in Training Deep Networks" by Hacohen & Weinshall (ICML 2019)

  Language:Python10825

• marcotcr/lime

  Lime: Explaining the predictions of any machine learning classifier

  Language:JavaScript11.7k1.8k