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..
InfinityGAN: Towards Infinite-Pixel Image Synthesis
Authors: Chieh Hubert Lin, Hsin-Ying Lee, Yen-Chi Cheng, Sergey Tulyakov, Ming-Hsuan Yang
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluation validates that Infinity GAN generates images with superior realism compared to baselines and features parallelizable inference. |
| Researcher Affiliation | Collaboration | 1UC Merced 2Snap Inc. 3Carnegie Mellon University 4Yonsei University 5Google Research |
| Pseudocode | Yes | Figure 33: Implementation of spatial style fusion. We present (left) the original Style GAN2 forward function, and (right) a corresponding implementation for the spatial style fusion. We align the related code blocks on the left and right. |
| Open Source Code | Yes | All codes, datasets, and trained models are publicly available. Project page: https://hubert0527.github.io/infinityGAN/ |
| Open Datasets | Yes | All codes, datasets, and trained models are publicly available. Project page: https://hubert0527.github.io/infinityGAN/ |
| Dataset Splits | Yes | For image outpainting task, we split the data into 80%, 10%, 10% for training, validation, and test. |
| Hardware Specification | Yes | Note that training and inference (of any size) are performed on a single GTX TITAN X GPU. ... We perform all the experiments on a workstation with Intel Xeon CPU (E5-2650 2.20GHz) and 8 GTX 2080Ti GPUs. |
| Software Dependencies | Yes | We implement our framework with Pytorch 1.6, and execute in an environment with Nvidia driver version 440.44, cu DNN version 4.6.5, and Cuda version 10.2.89. |
| Experiment Setup | Yes | We use λar = 1, λdiv = 1, λR1 = 10, and λpath = 2 for all datasets. All models are trained with 101 101 patches cropped from 197 197 real images. ... We adopt the Adam (Kingma & Ba, 2015) optimizer with β1 = 0, β2 = 0.99 and a batch size 16 for 800,000 iterations. |