EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations
Authors: Min Zhao, Fan Bao, Chongxuan LI, Jun Zhu
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we compare EGSDE to a large family of baselines on three widely-adopted unpaired I2I tasks under four metrics. |
| Researcher Affiliation | Collaboration | Min Zhao1, Fan Bao1, Chongxuan Li2,3 , Jun Zhu1 1Dept. of Comp. Sci. & Tech., BNRist Center, THU-Bosch ML Center, Tsinghua University, China 2 Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China 3 Beijing Key Laboratory of Big Data Management and Analysis Methods , Beijing, China 4 Pazhou Laboratory (Huangpu), Guangzhou, China |
| Pseudocode | Yes | Algorithm 1 EGSDE for unpaired image-to-image translation |
| Open Source Code | Yes | The code is available at https://github.com/ML-GSAI/EGSDE. |
| Open Datasets | Yes | Celeb A-HQ [20] contains high quality face images... AFHQ [8] consists of high-resolution animal face images... |
| Dataset Splits | No | The paper mentions using training and testing datasets for experiments but does not explicitly state the use of a separate validation dataset split. |
| Hardware Specification | Yes | Part of the computing resources supporting this work, totaled 500 A100 GPU hours, were provided by High-Flyer AI. (Hangzhou High-Flyer AI Fundamental Research Co., Ltd.). |
| Software Dependencies | No | The paper mentions using 'Euler-Maruyama solver' and implementing a 'resize function... by [45]', but does not provide specific software names with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | For generation process, by default, the weight parameter λs, λi is set 500 and 2 respectively. The initial time M and denoising steps N is set 0.5T and 500 by default. |