# FetaFix: Automatic Fault Localization and Repair of Deep Learning Model Conversions

arXiv: https://arxiv.org/abs/2312.15101 · HTML: https://mlsanity.com/paper/2312.15101/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 65 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [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)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [DeepLocalize: Fault Localization for Deep Neural Networks](https://mlsanity.com/paper/2103.03376/) (2021)
- [Repairing Deep Neural Networks: Fix Patterns and Challenges](https://mlsanity.com/paper/2005.00972/) (2020)
- [DeepFault: Fault Localization for Deep Neural Networks](https://mlsanity.com/paper/1902.05974/) (2019)
- [Exploring Effects of Computational Parameter Changes to Image Recognition Systems](https://mlsanity.com/paper/2211.00471/) (2022)
- [DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models](https://mlsanity.com/paper/2306.06208/) (2023)
- [Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition](https://mlsanity.com/paper/2306.06157/) (2023)

## Built on by (most cited first, 3 of 3)

- [DiTOX: Fault Detection and Localization in the ONNX Optimizer](https://mlsanity.com/paper/2505.01892/) (2025)
- [PoTAcc: A Pipeline for End-to-End Acceleration of Power-of-Two Quantized DNNs](https://mlsanity.com/paper/2605.06082/) (2026)
- [FAME: An FPGA-Based Platform for Approximate Multipliers Evaluation with Pattern-Guided DNN Retraining](https://mlsanity.com/paper/2609.17730/) (2026)

## Abstract (arXiv)

Converting deep learning models between frameworks is a common step to maximize model compatibility across devices and leverage optimization features that may be exclusively provided in one deep learning framework. However, this conversion process may be riddled with bugs, making the converted models either undeployable or problematic, considerably degrading their prediction correctness. In this paper, we propose an automated approach for fault localization and repair, FetaFix, during model conversion between deep learning frameworks. FetaFix is capable of detecting and fixing faults introduced in model input, parameters, hyperparameters, and the model graph during conversion. FetaFix uses a set of fault types (mined from surveying common conversion issues reported in code repositories and forums) to localize potential conversion faults in the converted target model and then repair them appropriately, e.g., replacing the parameters of the target model with those from the source model. This is done iteratively for every image in the dataset, comparing output label differences between the source model and the converted target model until all differences are resolved. We evaluate the effectiveness of FetaFix in fixing model conversion bugs of three widely used image recognition models converted across four different deep learning frameworks. Overall, FetaFix was able to fix 462 out of 755 detected conversion faults, either completely repairing or significantly improving the performance of 14 out of the 15 erroneous conversion cases.