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..
On Leveraging Pretrained GANs for Generation with Limited Data
Authors: Miaoyun Zhao, Yulai Cong, Lawrence Carin
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An extensive set of experiments is presented to demonstrate the effectiveness of the proposed techniques on generation with limited data. [...] Extensive experiments are conducted to verify the effectiveness of the proposed techniques. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, Duke University, Durham NC, USA. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at github.com/Miaoyun Zhao/GANTransfer Limited Data. |
| Open Datasets | Yes | Taking natural image generation as an illustrative example, we demonstrate the effectiveness of the proposed techniques by transferring the source GP-GAN model pretrained on the large-scale Image Net (containing 1.2 million images from 1,000 classes) to facilitate generation in perceptually-distinct target domains with (i) four smaller datasets, i.e., Celeb A (Liu et al., 2015) (202,599), Flowers (Nilsback & Zisserman, 2008) (8,189), Cars (Krause et al., 2013) (8,144), and Cathedral (Zhou et al., 2014) (7,350); (ii) their modified variants containing only 1,000 images; and (iii) two extremely limited datasets consisting of 25 images (following (Noguchi & Harada, 2019)). |
| Dataset Splits | No | The paper mentions using the 'whole Celeb A data for training' in Section 3.1.1 and different dataset sizes (e.g., 1,000 or 25 images) as the target data for experiments, but it does not specify explicit train/validation splits or percentages within these datasets for reproducibility. |
| Hardware Specification | Yes | The Titan Xp GPU used was donated by the NVIDIA Corporation. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies used in the experiments. |
| Experiment Setup | Yes | After 60,000 training iterations (generative quality stabilizes by then) [...] and apply GP (gradient penalty) on both real and fake samples to alleviate overfitting. |