Counterexample-Guided Data Augmentation
Authors: Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Kurt Keutzer, Alberto Sangiovanni-Vincentelli, Sanjit A. Seshia
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 {dreossi, shromona.ghosh, xyyue}@berkeley.edu, {keutzer, alberto, sseshia}@eecs.berkeley.edu |
| 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. |