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..
Efficient and Effective Augmentation Strategy for Adversarial Training
Authors: Sravanti Addepalli, Samyak Jain, Venkatesh Babu R
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We obtain improved robustness and large gains in standard accuracy on multiple datasets (CIFAR-10, CIFAR-100, Image Nette) and model architectures (RN-18, WRN-34-10). |
| Researcher Affiliation | Academia | Sravanti Addepalli Samyak Jain R.Venkatesh Babu Video Analytics Lab, Indian Institute of Science, Bangalore Indian Institute of Technology (BHU) Varanasi |
| Pseudocode | Yes | (Ref: Algorithm-1 in the Appendix) |
| Open Source Code | Yes | The code for implementing DAJAT is available here: https://github.com/val-iisc/DAJAT. |
| Open Datasets | Yes | We obtain improved robustness and large gains in standard accuracy on multiple datasets (CIFAR-10, CIFAR-100, Image Nette) and model architectures (RN-18, WRN-34-10). |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] Appendix-F.2 and Read Me file at https://github.com/val-iisc/DAJAT |
| Hardware Specification | Yes | Training time per epoch is reported by running each algorithm across 2 V100 GPUs. |
| Software Dependencies | No | The paper mentions software like 'PyTorch' in its references (e.g., [31]) but does not provide specific version numbers for any ancillary software or libraries used in its experiments within the main text or supplementary sections accessible without external links. |
| Experiment Setup | Yes | We train all models for 110 epochs unless specified otherwise. |