Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Counterexample-Guided Data Augmentation
Authors: Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Kurt Keutzer, Alberto Sangiovanni-Vincentelli, Sanjit A. Seshia
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. |
| Researcher Affiliation | Academia | Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Kurt Keutzer, Alberto Sangiovanni-Vincentelli and Sanjit A. Seshia University of California, Berkeley EMAIL, EMAIL |
| Pseudocode | No | The paper describes the augmentation scheme steps in paragraph text and presents a diagram (Figure 1), but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The implementation of the proposed framework and the reported experiments are available at https: //github.com/dreossi/analyze NN. |
| Open Datasets | No | In our experiments, we use synthetic data sets generated by our image generator. |
| Dataset Splits | No | The original training and test sets X and T contain 1500 and 750 pictures, respectively, randomly generated by our image generator. |
| Hardware Specification | Yes | We acknowledge the support of NVIDIA Corporation via the donation of the Titan Xp GPU used for this research. |
| Software Dependencies | No | The paper mentions using 'squeeze Det' and 'imgaug' (with a footnote link to its GitHub page: https://github.com/aleju/imgaug) but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | All models were trained for 65 epochs. |