Kepler codebook
Authors: Junrong Lian, Ziyue Dong, Pengxu Wei, Wei Ke, Chang Liu, Qixiang Ye, Xiangyang Ji, Liang Lin
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to evaluate our trained codebook for image reconstruction and generation on natural and human face datasets, respectively, achieving significant performance improvement. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China 2School of Software Engineering, Xi an Jiaotong University, Xi an, China 3Department of Automation, Tsinghua University, Beijing, China 4School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China 5Peng Cheng Laboratory, Shenzhen, China. |
| Pseudocode | No | The paper does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes and pre-trained weights are available at https://github.com/banianrong/Kepler Codebook. |
| Open Datasets | Yes | For empirical comparison with existing methods, we conduct the codebook training on ADE20K (Zhou et al., 2017) and Celeb A-HQ (Liu et al., 2015) datasets, respectively, in two tasks of image reconstruction and semantic image synthesis. |
| Dataset Splits | Yes | The evaluation results are reported on the validation sets of these two datasets, respectively. |
| Hardware Specification | Yes | All the experiments are conducted on 8 NVIDIA Tesla A100-40G GPUs. |
| Software Dependencies | No | The paper mentions "Adam W optimizer" but does not specify version numbers for any software or libraries used. |
| Experiment Setup | Yes | The model optimization is performed using the Adam W optimizer (Loshchilov & Hutter, 2017) with parameters β1 = 0.9 and β2 = 0.95, and a base learning rate of 4.5 × 10−6. The batch size is 96 for the reconstruction and 64 for the generation. |