albermax / innvestigate

Related projects: ⓘ

• marcoancona / DeepExplain
  A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability. DeepExplain also in…
  ☆727Updated 4 years ago
• sebastian-lapuschkin / lrp_toolbox
  The LRP Toolbox provides simple and accessible stand-alone implementations of LRP for artificial neural networks supporting Matlab and Py…
  ☆327Updated 2 years ago
• sicara / tf-explain
  Interpretability Methods for tf.keras models with Tensorflow 2.x
  ☆1,016Updated 3 months ago
• kundajelab / deeplift
  Public facing deeplift repo
  ☆818Updated 2 years ago
• understandable-machine-intelligence-lab / Quantus
  Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
  ☆526Updated last month
• pbiecek / xai_resources
  Interesting resources related to XAI (Explainable Artificial Intelligence)
  ☆816Updated 2 years ago
• ArrasL / LRP_for_LSTM
  Layer-wise Relevance Propagation (LRP) for LSTMs.
  ☆220Updated 4 years ago
• ankurtaly / Integrated-Gradients
  Attributing predictions made by the Inception network using the Integrated Gradients method
  ☆593Updated 2 years ago
• PAIR-code / saliency
  Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).
  ☆950Updated 6 months ago
• chr5tphr / zennit
  Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
  ☆191Updated 2 months ago
• pytorch / captum
  Model interpretability and understanding for PyTorch
  ☆4,810Updated this week
• marcotcr / anchor
  Code for "High-Precision Model-Agnostic Explanations" paper
  ☆794Updated 2 years ago
• philipperemy / keract
  Layers Outputs and Gradients in Keras. Made easy.
  ☆1,039Updated last month
• 1202kbs / Understanding-NN
  Tensorflow tutorial for various Deep Neural Network visualization techniques
  ☆345Updated 4 years ago
• lopusz / awesome-interpretable-machine-learning
  ☆903Updated last year
• tensorflow / tcav
  Code for the TCAV ML interpretability project
  ☆628Updated last month
• interpretml / DiCE
  Generate Diverse Counterfactual Explanations for any machine learning model.
  ☆1,335Updated 5 months ago
• gpleiss / temperature_scaling
  A simple way to calibrate your neural network.
  ☆1,081Updated 3 years ago
• stared / livelossplot
  Live training loss plot in Jupyter Notebook for Keras, PyTorch and others
  ☆1,294Updated 2 years ago
• SeldonIO / alibi
  Algorithms for explaining machine learning models
  ☆2,383Updated 2 months ago
• EthicalML / xai
  XAI - An eXplainability toolbox for machine learning
  ☆1,098Updated 2 years ago
• nitrain / nitrain
  Train AI models efficiently on medical images using any framework
  ☆1,860Updated 3 months ago
• eclique / RISE
  Detect model's attention
  ☆150Updated 4 years ago
• Trusted-AI / AIX360
  Interpretability and explainability of data and machine learning models
  ☆1,610Updated 2 months ago
• atulshanbhag / Layerwise-Relevance-Propagation
  Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers
  ☆96Updated 6 years ago
• modAL-python / modAL
  A modular active learning framework for Python
  ☆2,193Updated 6 months ago
• moboehle / Pytorch-LRP
  Basic LRP implementation in PyTorch
  ☆162Updated last month
• ufoym / imbalanced-dataset-sampler
  A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.
  ☆2,251Updated 2 months ago
• keisen / tf-keras-vis
  Neural network visualization toolkit for tf.keras
  ☆313Updated 5 months ago
• Lightning-Universe / lightning-bolts
  Toolbox of models, callbacks, and datasets for AI/ML researchers.
  ☆1,678Updated this week