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..
Image Generation using Continuous Filter Atoms
Authors: Ze Wang, Seunghyun Hwang, Zichen Miao, Qiang Qiu
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We support the proposed framework of image generation with continuous filter atoms using various experiments, including image-to-image translation and image generation conditioned on continuous labels. |
| Researcher Affiliation | Academia | Ze Wang Seunghyun Hwang Zichen Miao Qiang Qiu Purdue University EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that the code will be made publicly available. |
| Open Datasets | Yes | We present results on various datasets including edges ! handbags, edges ! shoes, maps ! satellite, night ! days, and labels ! facades. Paired samples. In this experiment, we plug in our continuous filter atoms to the Pix2Pix [17] model and conduct continuous conditional image-to-image translation tasks on five different synthetic datasets, RC-49 [8], RA-20, RCA-20, RT-20, RL-20, where objects in each dataset are rendered to rotate between 0 and 360 with 0.1 interval. Unpaired samples. For translation tasks on unpaired datasets, we present results on UTKFace [56], Steering Angle, cells-200, and Waymo [41] dataset. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits with specific percentages, counts, or references to predefined split methodologies. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide a reproducible description of ancillary software, as no specific version numbers for key software components are mentioned. |
| Experiment Setup | No | Implementation details are in Appendix Section B. |