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..
Stabilizing GANs’ Training with Brownian Motion Controller
Authors: Tianjiao Luo, Ziyu Zhu, Jianfei Chen, Jun Zhu
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that our GANs-BMC effectively stabilizes GANs training under Style GANv2-ada frameworks with a faster rate of convergence, a smaller range of oscillation, and better performance in terms of FID score. |
| Researcher Affiliation | Collaboration | 1Dept. of Comp. Sci. & Tech., Institute for AI, BNRist Center, Tsinghua-Bosch Joint ML Center, THBI Lab, Tsinghua University 2Pazhou Lab (Huangpu), Guangzhou, China. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for its methodology. |
| Open Datasets | Yes | We evaluate our proposed GANs-BMC on well-established CIFAR10 (Krizhevsky et al., 2009), LSUNBedroom with resolution 256x256 (Yu et al., 2015), LSUN-Cat with resolution 256x256 (Yu et al., 2015), and FFHQ with resolution 1024x1024 (Karras et al., 2019). |
| Dataset Splits | Yes | We reproduce the identical configuration settings as reported in the Style GANv2-ada paper within the period of 7 days on 4 cards of NVIDIA Ge Force GTX TITAN X. The detailed experimental setups can be found in Appendix C. (Implicitly uses standard splits for well-known datasets like CIFAR-10 and FFHQ which are typically split for training/validation/testing) |
| Hardware Specification | Yes | We reproduce the identical configuration settings as reported in the Style GANv2-ada paper within the period of 7 days on 4 cards of NVIDIA Ge Force GTX TITAN X. |
| Software Dependencies | No | The paper mentions software like Style GANv2-ada and the Adam optimizer, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The detailed experimental setups can be found in Appendix C. Table 4 and Table 5 provide details such as Dataset, Batch Size, Learning Rate, Optimizer, and GPUs used. |