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..
Dynamically Grown Generative Adversarial Networks
Authors: Lanlan Liu, Yuting Zhang, Jia Deng, Stefano Soatto8680-8687
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate new state-of-the-art of image generation. We evaluate DGGAN against manually designed Prog GAN and other recent GAN models on CIFAR-10 and LSUN. |
| Researcher Affiliation | Collaboration | Lanlan Liu*, 1 Yuting Zhang, 2 Jia Deng, 3 Stefano Soatto 2 1 University of Michigan, Ann Arbor 2 Amazon Web Services 3 Princeton University |
| Pseudocode | Yes | Algorithm 1: Top-K Greedy Pruning Algorithm |
| Open Source Code | No | The paper refers to using "the most popular Py Torch implementation1of Prog GAN" (Footnote 1: https://github.com/facebookresearch/pytorchGANzoo) to implement their DGGAN, but it does not explicitly state that their own DGGAN source code is released or available at this or any other link. |
| Open Datasets | Yes | CIFAR-10 (Krizhevsky 2009) contains 50k 32 32 training images. LSUN (Yu et al. 2015) has over a million 256 256 bedroom images for training. |
| Dataset Splits | No | The paper mentions datasets used for training but does not specify training, validation, or test splits or percentages for reproducing the data partitioning. |
| Hardware Specification | No | The paper states: "The resulting computational cost is 580 GPU days for 2k+ CIFAR-10 models and 1720 GPU days for 1k+ LSUN models." This indicates the use of GPUs but does not specify exact models, manufacturers, or other hardware details. |
| Software Dependencies | No | The paper states: "We use the most popular Py Torch implementation1of Prog GAN to obtain comprehensive Prog GAN results and to implement our DGGAN." It mentions PyTorch but does not provide a specific version number for PyTorch or any other software dependency. |
| Experiment Setup | Yes | We train initial candidates for 100k iterations and train each new candidate with 100k iterations after weight inheritance. We gradually increase the resolution from d0 = 8 to 32. After reaching the ๏ฌnal resolution, following (Karras et al. 2018), we further train the ๏ฌxed architecture longer to achieve convergence. We follow the same training schedule as Prog GAN. |