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..
Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
Authors: Yawei Luo, Ping Liu, Tao Guan, Junqing Yu, Yi Yang
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both cross-domain classification and segmentation benchmarks verify that ASM achieves state-of-the-art adaptation performance under the challenging one-shot setting. |
| Researcher Affiliation | Collaboration | 1School of Computer Science & Technology, Huazhong University of Science & Technology 2CCAI, Zhejiang University 3Baidu Research 4Re LER, University of Technology Sydney 5Institute of High Performance Computing, A*STAR, Singapore |
| Pseudocode | Yes | Algorithm 1: Adversarial Style Mining |
| Open Source Code | Yes | The code is publicly available at https://github. com/Royal Vane/ASM. |
| Open Datasets | Yes | We use MNIST [21]-USPS [16]-SVHN [35] benchmarks to evaluate ASM on one-shot cross domain classification task. For one-shot cross-domain segmentation task, we evaluate ASM on two benchmarks, i.e., SYNTHIA [37] Cityscapes [5] and GTA5 [36] Cityscapes. |
| Dataset Splits | No | We run each OSUDA experiment for 5 times to get the average result, where each time we randomly select one-shot sample from the target domain. The paper describes how the one-shot sample is selected but does not specify general train/validation/test splits for the full datasets used. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used, such as GPU or CPU models. |
| Software Dependencies | No | We evaluate ASM together with several state-of-the-art UDA algorithms on both classification and segmentation tasks using Paddle Paddle and Pytorch. The paper mentions frameworks but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | More details on experimental settings are given in Appendix A and B. The main text does not include specific hyperparameters or system-level training settings. |