Stabilizing GANs’ Training with Brownian Motion Controller
Authors: Tianjiao Luo, Ziyu Zhu, Jianfei Chen, Jun Zhu
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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. |