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..
Understanding and Stabilizing GANs’ Training Dynamics Using Control Theory
Authors: Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that our method can effectively stabilize the training and obtain state-of-the-art performance on data generation tasks. |
| Researcher Affiliation | Collaboration | 1Dept. of Comp. Sci. & Tech., Institute for AI, BNRist Center, Tsinghua-Bosch ML Center, THBI Lab, Tsinghua University, Beijing, China. |
| Pseudocode | Yes | Algorithm 1 Cloosed-loop Control GAN |
| Open Source Code | No | Our code is provided HERE. (Note: 'HERE' is a placeholder and not an actual link to a repository) |
| Open Datasets | Yes | We now empirically verify our method on the widely-adopted CIFAR10 (Krizhevsky et al., 2009) and Celeb A (Liu et al., 2015) datasets. |
| Dataset Splits | No | The paper mentions using CIFAR10 and Celeb A datasets but does not explicitly provide details about the validation split (e.g., percentages, counts, or specific references to predefined validation sets used in their experiments). |
| Hardware Specification | Yes | For instance, our method can conduct approximate 8 iterations per second of training on Celeb A whereas Reg-GAN can only conduct 4 iterations per second on Geforce 1080Ti. |
| Software Dependencies | No | The paper mentions architectures like ResNet and ReLU activation, but it does not provide specific version numbers for any software dependencies (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | The batch size is 64, and the buffer size Nb is set to be 100 times of the batch size for all settings. We manually select the coefficient λ among {1, 2, 5, 10, 15, 20} in Reg-GAN s setting and among {0.05, 0.1, 0.2, 0.5} in SN-GAN s setting. |