129 Open source projects that are alternatives of or similar to Pytorch Grad Cam

A curated list of awesome machine learning interpretability resources.

COVID-CXNet: Diagnosing COVID-19 in Frontal Chest X-ray Images using Deep Learning. Preprint available on arXiv: https://arxiv.org/abs/2006.13807

Code for NeurIPS 2019 paper ``Self-Critical Reasoning for Robust Visual Question Answering''

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

Fit interpretable models. Explain blackbox machine learning.

Model interpretability and understanding for PyTorch

In this part, I've introduced and experimented with ways to interpret and evaluate models in the field of image. (Pytorch)

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

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

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

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

InterpretDL: Interpretation of Deep Learning Models，基于『飞桨』的模型可解释性算法库。

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

Code for the TCAV ML interpretability project

Text classification models. Used a submodule for other projects.

H2O.ai Machine Learning Interpretability Resources

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)

Awesome Resources for Advanced Computer Vision Topics

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

Plotting library for IPython/Jupyter notebooks

Human-explainable AI.

Algorithms for monitoring and explaining machine learning models

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

[NeurIPS'21] "Chasing Sparsity in Vision Transformers: An End-to-End Exploration" by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang

A hybrid command-line/UI development experience for cloud-native development

Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.

moDel Agnostic Language for Exploration and eXplanation

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

Public facing deeplift repo

💡 A curated list of adversarial attacks on model explanations

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

Model Agnostics breakDown plots

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

General Interpretability Package

A curated list of awesome Fairness in AI resources

Your window into the Elastic Stack

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

Visualizing where the Convolution Network is looking through CAM.

Code repository for "Interpretable and Accurate Fine-grained Recognition via Region Grouping", CVPR 2020 (Oral)

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

[ICCV 2017] Torch code for Grad-CAM

Faster and more precisely than Grad-CAM

Layer-wise Relevance Propagation (LRP) for LSTMs

Git Repo for Articles on Ergo Sum blog and the youtube channel https://www.youtube.com/channel/UCiie9CN--dazA7iT2sry5FA

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

A basic, free tool that shows information about Plex Media Server content

Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers

A collection of tools for reverse engineering neural networks.

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.

✂️ Repository for our ICLR 2019 paper: Discovery of Natural Language Concepts in Individual Units of CNNs

Federated Learning Library: https://fedml.ai

Magic to display dynamic call graphs of Python function calls

Towards Understanding Deep Learning Representations via Interactive Experimentation

Pytorch Implementation of recent visual attribution methods for model interpretability

🏥 Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer’s Disease

Pytorch implementation of convolutional neural network visualization techniques

A hybrid command-line/UI development experience for cloud-native development

XAI - An eXplainability toolbox for machine learning

A library for finding knowledge neurons in pretrained transformer models.

Visualizations supporting HTM School