Low-Rank Subspaces in GANs
Authors: Jiapeng Zhu, Ruili Feng, Yujun Shen, Deli Zhao, Zheng-Jun Zha, Jingren Zhou, Qifeng Chen
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on state-of-the-art GAN models (including Style GAN2 and Big GAN) trained on various datasets demonstrate the effectiveness of our Low Rank GAN. |
| Researcher Affiliation | Collaboration | Jiapeng Zhu1 Ruili Feng2,3 Yujun Shen4 Deli Zhao2 Zheng-Jun Zha3 Jingren Zhou2 Qifeng Chen1 1Hong Kong University of Science and Technology 2Alibaba Group 3University of Science and Technology of China 4Byte Dance Inc. |
| Pseudocode | No | The paper describes mathematical formulations and processes, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/zhujiapeng/Low Rank GAN/. |
| Open Datasets | Yes | The datasets we use are diverse, including FFHQ [18], LSUN [36] and Image Net [9]. |
| Dataset Splits | No | The paper mentions using datasets for experiments but does not provide specific details on how the data was split into training, validation, and test sets (e.g., percentages or sample counts for each split). |
| Hardware Specification | No | The paper does not specify the hardware used for running experiments (e.g., specific GPU or CPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper mentions models like StyleGAN2 and BigGAN, and segmentation models like [20] and Pixel Lib, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Note that in the experiments of car and church, for the small mask regions, a relatively larger rrelax is needed. Here we set rrelax = 20. |