99 Open source projects that are alternatives of or similar to ConceptBottleneck

Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University

Codebase for "Demystifying Black-box Models with Symbolic Metamodels", NeurIPS 2019.

Algorithms for monitoring and explaining machine learning models

[ICCV 2017] Torch code for Grad-CAM

moDel Agnostic Language for Exploration and eXplanation

Interpretability Methods for tf.keras models with Tensorflow 2.x

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

Federated Learning Library: https://fedml.ai

XAI - An eXplainability toolbox for machine learning

Visualization toolkit for neural networks in PyTorch! Demo -->

Interesting resources related to XAI (Explainable Artificial Intelligence)

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

Public facing deeplift repo

Code for the TCAV ML interpretability project

A collection of infrastructure and tools for research in neural network interpretability.

H2O.ai Machine Learning Interpretability Resources

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

深度学习近年来关于神经网络模型解释性的相关高引用/顶会论文(附带代码)

Human-explainable AI.

Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

A lightweight implementation of removal-based explanations for ML models.

Code for SPINE - Sparse Interpretable Neural Embeddings. Jhamtani H.*, Pruthi D.*, Subramanian A.*, Berg-Kirkpatrick T., Hovy E. AAAI 2018

Towards Understanding Deep Learning Representations via Interactive Experimentation

A library for finding knowledge neurons in pretrained transformer models.

[ECCV 2018] code for Choose Your Neuron: Incorporating Domain Knowledge Through Neuron Importance

🏔️ Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations

For calculating global feature importance using Shapley values.

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

official implementation of "Visualization of Convolutional Neural Networks for Monocular Depth Estimation"

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

Python package to visualize and cluster partial dependence.

Code for "Free-Lunch Saliency via Attention in Atari Agents"

Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers

FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

Fast approximate Shapley values in R

Implementation of the paper "Shapley Explanation Networks"

A Julia package for interpretable machine learning with stochastic Shapley values