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..
E$^2$GAN: Efficient Training of Efficient GANs for Image-to-Image Translation
Authors: Yifan Gong, Zheng Zhan, Qing Jin, Yanyu Li, Yerlan Idelbayev, Xian Liu, Andrey Zharkov, Kfir Aberman, Sergey Tulyakov, Yanzhi Wang, Jian Ren
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments In this section, we provide the detailed experimental settings and results of our proposed method. |
| Researcher Affiliation | Collaboration | Yifan Gong * 1 2 Zheng Zhan * 2 Qing Jin 1 Yanyu Li 1 2 Yerlan Idelbayev 2 Xian Liu 1 Andrey Zharkov 1 Kfir Aberman 1 Sergey Tulyakov 1 Yanzhi Wang 2 Jian Ren 1 *Equal contribution, work is done during Yifan s internship at Snap Inc. 1Snap Inc. 2Northeastern University. Correspondence to: Jian Ren <EMAIL>. |
| Pseudocode | Yes | The overall algorithm is described in Algorithm 1 in Sec. A in the Appendix. |
| Open Source Code | No | Project Page: https://yifanfanfanfan.github.io/e2gan/. The project page is mentioned, but there is no explicit statement that the source code for the methodology described in the paper is available there, nor is it a direct link to a code repository. |
| Open Datasets | Yes | We verify our method on 1,000 images from FFHQ dataset (Karras et al., 2019) and Flickr Scenery dataset (Cheng et al., 2022) with image resolution as 256 256. |
| Dataset Splits | Yes | To perform training and evaluation of GAN models, we divide the image pairs from each target concept into training/validation/test subsets with the ratio as 80%/10%/10%. |
| Hardware Specification | Yes | The training and training time measurements are conducted on one NVIDIA H100 GPU with 80 GB memory. |
| Software Dependencies | No | The paper mentions using the Adam solver, but does not provide specific version numbers for software libraries or environments like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | The training is conducted from an initial learning rate of 2e-4 with mini-batch SGD using Adam solver (Kingma & Ba, 2014). The total training epochs is set to 100 for E2GAN, and 200 for pix2pix (Isola et al., 2017) and pix2pix-zero-distilled (Parmar et al., 2023) for them to converge well. |