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..
Enhance the Visual Representation via Discrete Adversarial Training
Authors: Xiaofeng Mao, YueFeng Chen, Ranjie Duan, Yao Zhu, Gege Qi, shaokai ye, Xiaodan Li, Rong Zhang, Hui Xue'
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment Discrete Adversarial Training (DAT) on multiple tasks including image classification, object detection and self-supervised learning. |
| Researcher Affiliation | Collaboration | Alibaba Group, Zhejiang University, EPFL EMAIL |
| Pseudocode | Yes | Algorithm 1: Pseudo code of DAT |
| Open Source Code | Yes | The code will be available at https://github.com/alibaba/easyrobust. |
| Open Datasets | Yes | We adopt Image Net-1K for both training and indistribution testing. |
| Dataset Splits | Yes | We study this effect by sampling 1000 mini-batches in Image Net validation set |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for experiments, such as GPU models, CPU types, or memory. |
| Software Dependencies | No | We implement DAT with vanilla training recipes using "robustness" library. |
| Experiment Setup | Yes | We set = 0.1 by default in DAT. |