128 Open source projects that are alternatives of or similar to Adversarial Explainable Ai

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

Improving Document Binarization via Adversarial Noise-Texture Augmentation

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

Fit interpretable models. Explain blackbox machine learning.

For calculating global feature importance using Shapley values.

Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)

Towards Understanding Deep Learning Representations via Interactive Experimentation

Algorithms for monitoring and explaining machine learning models

Scaleable input gradient regularization

Public facing deeplift repo

Adversarial Learning for Semi-supervised Semantic Segmentation, BMVC 2018

Implementation of 'Self-Adversarial Variational Autoencoder with Gaussian Anomaly Prior Distribution for Anomaly Detection'

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

Human-explainable AI.

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

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

Federated Learning Library: https://fedml.ai

A library for finding knowledge neurons in pretrained transformer models.

A curated list of awesome graph representation learning.

Guiding Entity Alignment via Adversarial Knowledge Embedding

Interesting resources related to XAI (Explainable Artificial Intelligence)

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

A Toolbox for Adversarial Robustness Research

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

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

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)

Implementation of "Disentangled Representation Learning for Non-Parallel Text Style Transfer(ACL 2019)" in Pytorch

Training neural models with structured signals.

[CVPR 2019 Oral] Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation

[CVPR 2018] Look at Boundary: A Boundary-Aware Face Alignment Algorithm

Implementation of Papers on Adversarial Examples

Next RecSys Library

Awesome Domain Adaptation Python Toolbox

Code of Gradually Vanishing Bridge for Adversarial Domain Adaptation (CVPR2020)

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

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.

[IJCAI 2021] Cross-Domain Few-Shot Classification via Adversarial Task Augmentation

Take further steps in the arms race of adversarial examples with only preprocessing.

Gym environments modified with adversarial agents

XAI - An eXplainability toolbox for machine learning

🗣️ NALP is a library that covers Natural Adversarial Language Processing.

General Interpretability Package

Attending to Discriminative Certainty for Domain Adaptation

Domain-Adversarial Neural Network in Tensorflow

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

[ICCV 2017] Torch code for Grad-CAM

PyTorch reimplementation of the paper: "Semantic Pyramid for Image Generation" [CVPR 2020].

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

Reimplementing cool papers in PyTorch...

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

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

Code for the TCAV ML interpretability project

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

Must-read Papers on Textual Adversarial Attack and Defense

H2O.ai Machine Learning Interpretability Resources

Text classification models. Used a submodule for other projects.

[CVPR 2019] "Handwriting Recognition in Low-resource Scripts using Adversarial Learning ”, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2019.

Adversarial Background-Aware Loss for Weakly-supervised Temporal Activity Localization (ECCV 2020)

moDel Agnostic Language for Exploration and eXplanation

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