DeepExposure: Learning to Expose Photos with Asynchronously Reinforced Adversarial Learning
Authors: Runsheng Yu, Wenyu Liu, Yasen Zhang, Zhi Qu, Deli Zhao, Bo Zhang
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The extensive experiments verify that our algorithms are superior to state-of-the-art methods in terms of quantitative accuracy and visual illustration. 5 Experiment |
| Researcher Affiliation | Collaboration | Runsheng Yu Xiaomi AI Lab South China Normal University runshengyu@gmail.com Wenyu Liu Xiaomi AI Lab Peking University liuwenyu@pku.edu.cn Yasen Zhang Xiaomi AI Lab zhangyasen@xiaomi.com Zhi Qu Xiaomi AI Lab quzhi@xiaomi.com Deli Zhao Xiaomi AI Lab zhaodeli@xiaomi.com Bo Zhang Xiaomi AI Lab zhangbo@xiaomi.com |
| Pseudocode | Yes | The pseudo-codes are presented in Appendix E. |
| Open Source Code | No | The paper does not provide a specific repository link or an explicit statement about releasing the source code for their methodology. It only mentions using pre-trained models or demos from other works. |
| Open Datasets | Yes | We train our model on MIT-Adobe Five K [3], a dataset which contains 5,000 RAW photos and corresponding retouched ones edited by five experts for each photo. |
| Dataset Splits | No | The paper states: 'We separate the dataset into three subsets: 2, 000 input unretouched images, 2, 000 retouched images by expert C, and 1, 000 input RAW images for testing.' It does not explicitly mention a validation split or provide specific details for one. |
| Hardware Specification | Yes | The codes are run on P40 Tesla GPU. |
| Software Dependencies | No | The paper states 'All the networks are implemented via Tensorflow.' but does not provide a specific version number for Tensorflow or any other software dependencies. |
| Experiment Setup | Yes | Here we present some details between different networks. For discriminator network, the original learning rate is 5 10 5 with an exponential decay to 10 3 of the original value. The batch size for adversarial learning is 8. For policy network, the original learning rate is 1.5 10 5 with an exponential decay to 10 3 of the original value. The Ornstein-Uhlenbeck process [34] is used to perform the exploration 2. The mini-batch size for policy network is 8. ... For value network, if it is Deep Exposure I, the original learning rate is 5 10 4 with an exponential decay to 10 3 of the original value. ... The γ parameter is set 0.99. |