Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
☆666May 4, 2026Updated last month
Alternatives and similar repositories for Quantus
Users that are interested in Quantus are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- MetaQuantus is an XAI performance tool to identify reliable evaluation metrics☆44Apr 17, 2024Updated 2 years ago
- Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.☆246May 13, 2026Updated last month
- OpenXAI : Towards a Transparent Evaluation of Model Explanations☆255Aug 17, 2024Updated last year
- An eXplainable AI toolkit with Concept Relevance Propagation and Relevance Maximization☆144Jan 14, 2026Updated 5 months ago
- Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models. Paper presented at MICCAI 2023 conference.☆20Jan 17, 2024Updated 2 years ago
- GPUs on demand by Runpod - Special Offer Available • AdRun AI, ML, and HPC workloads on powerful cloud GPUs—without limits or wasted spend. Deploy GPUs in under a minute and pay by the second.
- Layer-wise Relevance Propagation for Large Language Models and Vision Transformers [ICML 2024]☆239Jul 11, 2025Updated 11 months ago
- 👋 Xplique is a Neural Networks Explainability Toolbox☆747Mar 31, 2026Updated 3 months ago
- CoRelAy is a tool to compose small-scale (single-machine) analysis pipelines.☆32Apr 30, 2026Updated 2 months ago
- Source Code of the ROAD benchmark for feature attribution methods (ICML22)☆25Jun 26, 2023Updated 3 years ago
- Explainable AI in Julia.☆118Jun 22, 2026Updated last week
- Prototypical Concept-based Explanations, accepted at SAIAD workshop at CVPR 2024.☆16Feb 20, 2026Updated 4 months ago
- NoiseGrad (and its extension NoiseGrad++) is a method to enhance explanations of artificial neural networks by adding noise to model weig…☆22May 11, 2023Updated 3 years ago
- [NeurIPS 2024] CoSy is an automatic evaluation framework for textual explanations of neurons.☆20Jan 28, 2026Updated 5 months ago
- Interpretability and explainability of data and machine learning models☆1,781May 22, 2026Updated last month
- Managed hosting for WordPress and PHP on Cloudways • AdManaged hosting for WordPress, Magento, Laravel, or PHP apps, on multiple cloud providers. Deploy in minutes on Cloudways by DigitalOcean.
- ☆17Jun 3, 2026Updated 3 weeks ago
- A Python framework for the quantitative evaluation of eXplainable AI methods☆18Mar 29, 2023Updated 3 years ago
- Model interpretability and understanding for PyTorch☆5,661Jun 23, 2026Updated last week
- Concept Relevance Propagation for Localization Models, accepted at SAIAD workshop at CVPR 2023.☆15Jan 16, 2024Updated 2 years ago
- A collection of research materials on explainable AI/ML☆1,650Mar 7, 2026Updated 3 months ago
- Repository for PURE: Turning Polysemantic Neurons Into Pure Features by Identifying Relevant Circuits, accepted at CVPR 2024 XAI4CV Works…☆20May 29, 2024Updated 2 years ago
- ☆14Jun 12, 2023Updated 3 years ago
- [NeurIPS 2025 MechInterp Workshop - Spotlight] Official implementation of the paper "RelP: Faithful and Efficient Circuit Discovery in La…☆29Nov 3, 2025Updated 7 months ago
- Algorithms for explaining machine learning models☆2,635Oct 17, 2025Updated 8 months ago
- Bare Metal GPUs on DigitalOcean Gradient AI • AdPurpose-built for serious AI teams training foundational models, running large-scale inference, and pushing the boundaries of what's possible.
- Official Code Implementation of the paper : XAI for Transformers: Better Explanations through Conservative Propagation☆67Feb 14, 2022Updated 4 years ago
- OmniXAI: A Library for eXplainable AI☆969Jun 2, 2026Updated 3 weeks ago
- B-LRP is the repository for the paper How Much Can I Trust You? — Quantifying Uncertainties in Explaining Neural Networks☆18Jun 22, 2022Updated 4 years ago
- Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems☆78Mar 26, 2022Updated 4 years ago
- ☆30Jan 30, 2025Updated last year
- InterpretDL: Interpretation of Deep Learning Models，基于『飞桨』的模型可解释性算法库。☆261Sep 4, 2024Updated last year
- Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).☆997Mar 20, 2024Updated 2 years ago
- 'Robust Semantic Interpretability: Revisiting Concept Activation Vectors' Official Implementation☆11Jul 15, 2020Updated 5 years ago
- [XAI4CV CVPR 2023] Towards Evaluating Explanations of Vision Transformers for Medical Imaging☆10Dec 1, 2023Updated 2 years ago
- Wordpress hosting with auto-scaling - Free Trial Offer • AdFully Managed hosting for WordPress and WooCommerce businesses that need reliable, auto-scalable performance. Cloudways SafeUpdates now available.
- [NeurIPS 2024] Official implementation of the paper "MambaLRP: Explaining Selective State Space Sequence Models" 🐍☆47Nov 6, 2024Updated last year
- Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).☆1,593May 26, 2026Updated last month
- How to predict extreme events in climate using rare event algorithms and modern tools of machine learning☆25Mar 27, 2025Updated last year
- xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology☆35Apr 27, 2026Updated 2 months ago
- Code for RELAX, a framework for explaining representations.☆12Jan 7, 2024Updated 2 years ago
- Detect model's attention☆176Jul 2, 2020Updated 5 years ago
- HIVE: Evaluating the Human Interpretability of Visual Explanations (ECCV 2022)☆22Jan 19, 2023Updated 3 years ago