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..
Training Generative Adversarial Networks with Limited Data
Authors: Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, Timo Aila
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate, on several datasets, that good results are now possible using only a few thousand training images, often matching Style GAN2 results with an order of magnitude fewer images. We also find that the widely used CIFAR-10 is, in fact, a limited data benchmark, and improve the record FID from 5.59 to 2.42. |
| Researcher Affiliation | Collaboration | Tero Karras NVIDIA EMAIL Miika Aittala NVIDIA EMAIL Janne Hellsten NVIDIA EMAIL Samuli Laine NVIDIA EMAIL Jaakko Lehtinen NVIDIA and Aalto University EMAIL Timo Aila NVIDIA EMAIL |
| Pseudocode | No | The paper includes flowcharts in Figure 2, but no structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation and models are available at https://github.com/NVlabs/stylegan2-ada |
| Open Datasets | Yes | For our baseline, we considered Style GAN2 [19] and Big GAN [5, 30]. We measure quality by computing FID between 50k generated images and all available training images, as recommended by Heusel et al. [16], regardless of the subset actually used for training. |
| Dataset Splits | No | The paper mentions using a "separate validation set" and discusses its behavior for quantifying overfitting, but does not specify the exact split percentages or number of samples for this validation set. |
| Hardware Specification | Yes | but runs 4.6x faster on NVIDIA DGX-1. |
| Software Dependencies | No | The paper mentions software like StyleGAN2 and BigGAN but does not provide specific version numbers for underlying libraries or frameworks (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We use 2 fewer feature maps, 2x larger minibatch, mixed-precision training for layers at 32^2, η = 0.0025, γ = 1, and exponential moving average half-life of 20k images for generator weights. |