# AIREPAIR: A Repair Platform for Neural Networks

arXiv: https://arxiv.org/abs/2211.15387 · HTML: https://mlsanity.com/paper/2211.15387/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 24 Nov 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Editable Neural Networks](https://mlsanity.com/paper/2004.00345/) (2020)
- [Provable Repair of Deep Neural Networks](https://mlsanity.com/paper/2104.04413/) (2021)
- [Arachne: Search Based Repair of Deep Neural Networks](https://mlsanity.com/paper/1912.12463/) (2019)
- [Are Adversarial Robustness and Common Perturbation Robustness Independent Attributes ?](https://mlsanity.com/paper/1909.02436/) (2019)
- [DeepCorrect: Correcting DNN models against Image Distortions](https://mlsanity.com/paper/1705.02406/) (2017)
- [Counterexample-Guided Data Augmentation](https://mlsanity.com/paper/1805.06962/) (2018)

## Built on by (most cited first, 4 of 4)

- [LUNA: A Model-Based Universal Analysis Framework for Large Language Models](https://mlsanity.com/paper/2310.14211/) (2023)
- [A Comprehensive Study of Deep Learning Model Fixing Approaches](https://mlsanity.com/paper/2512.23745/) (2025)
- [QNNRepair: Quantized Neural Network Repair](https://mlsanity.com/paper/2306.13793/) (2023)
- [Shapley-Guided Neural Repair Approach via Derivative-Free Optimization](https://mlsanity.com/paper/2604.00422/) (2026)

## Abstract (arXiv)

We present AIREPAIR, a platform for repairing neural networks. It features the integration of existing network repair tools. Based on AIREPAIR, one can run different repair methods on the same model, thus enabling the fair comparison of different repair techniques. We evaluate AIREPAIR with three state-of-the-art repair tools on popular deep-learning datasets and models. Our evaluation confirms the utility of AIREPAIR, by comparing and analyzing the results from different repair techniques. A demonstration is available at https://youtu.be/UkKw5neeWhw.