mlsanityData to 7 Oct 2026

Counterexample-Guided Data Augmentation

arXiv 1805.06962 ↗First posted 17 May 2018cs.LG cs.AI stat.MLcited by 15

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Abstract (arXiv)

We present a novel framework for augmenting data sets for machine learning based on counterexamples. Counterexamples are misclassified examples that have important properties for retraining and improving the model. Key components of our framework include a counterexample generator, which produces data items that are misclassified by the model and error tables, a novel data structure that stores information pertaining to misclassifications. Error tables can be used to explain the model's vulnerabilities and are used to efficiently generate counterexamples for augmentation. We show the efficacy of the proposed framework by comparing it to classical augmentation techniques on a case study of object detection in autonomous driving based on deep neural networks.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 May 2018.

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Citing papers per year: 2021: 6; 2022: 4; 2023: 1; 2024: 2; 2025: 2.

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  1. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  2. 2024Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systemscited by 94
  3. 2021Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentationcited by 56
  4. 2021Taxonomy of Machine Learning Safety: A Survey and Primercited by 22
  5. 2021Sound and Complete Neural Network Repair with Minimality and Locality Guaranteescited by 12
  6. 2022Correct-by-Construction Runtime Enforcement in AI -- A Surveycited by 10
  7. 2022AIREPAIR: A Repair Platform for Neural Networkscited by 4
  8. 2025Hierarchical Testing with Rabbit Optimization for Industrial Cyber-Physical Systemscited by 4
  9. 2021Local Repair of Neural Networks Using Optimizationcited by 3
  10. 2023Mitigating Data Scarcity for Large Language Modelscited by 2
  11. 2021Generalizing Neural Networks by Reflecting Deviating Data in Productioncited by 0
  12. 2022Confidence-Guided Data Augmentation for Improved Semi-Supervised Trainingcited by 0
3 more
  1. 2022A Machine Learning Approach to Classifying Construction Cost Documents into the International Construction Measurement Standardcited by 0
  2. 2024Generational Computation Reduction in Informal Counterexample-Driven Genetic Programmingcited by 0
  3. 2025A Customer Journey in the Land of Oz: Leveraging the Wizard of Oz Technique to Model Emotions in Customer Service Interactionscited by 0