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..
Neural FFTs for Universal Texture Image Synthesis
Authors: Morteza Mardani, Guilin Liu, Aysegul Dundar, Shiqiu Liu, Andrew Tao, Bryan Catanzaro
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations confirm that our method achieves state-of-the-art performance both quantitatively and qualitatively. (Abstract) |
| Researcher Affiliation | Industry | Morteza Mardani , Guilin Liu , Aysegul Dundar, Shiqiu Liu, Andrew Tao, Bryan Catanzaro NVIDIA EMAIL |
| Pseudocode | No | The paper describes the network architecture and training process in textual descriptions and diagrams, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing its source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | A large texture dataset with 55, 583 images from 15 different sources [8, 53, 9, 6, 7, 47, 1, 15, 45, 32] are collected. (Section 5) |
| Dataset Splits | Yes | The dataset is randomly split into a training set of 49, 583 images, a validation set of 1, 000 images, and a test set of 5, 000 images. (Section 5) |
| Hardware Specification | Yes | The model was trained on 4 DGX-1 stations with 32 total NVIDIA Tesla V100 GPUs and 320 CPUs using synchronized batch normalization layers [25]. (Section 5.1) |
| Software Dependencies | No | The paper mentions 'Pytorch interface with cu DNN' but does not specify version numbers for either software component. |
| Experiment Setup | Yes | We choose batch size of 8 per GPU, and the initial learning rate 10 5 that is halved every 200 epochs. Total of 800 epochs are used for convergence. We also set λvgg = 0.1, λstyle = 200, λadv = 0.1. (Section 5.1) |