Region-Based Semantic Factorization in GANs
Authors: Jiapeng Zhu, Yujun Shen, Yinghao Xu, Deli Zhao, Qifeng Chen
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to evaluate our proposed method, mainly on two types of models, i.e., Style GAN2 (Karras et al., 2020b) and Big GAN (Brock et al., 2019). And the datasets we use are diverse, including FFHQ (Karras et al., 2019), LSUN bedroom, church, car (Yu et al., 2015), and Image Net (Deng et al., 2009). For metrics, we use Fr echet Inception Distance (FID) (Heusel et al., 2017), masked Mean Squared Error (MSE), and Identity loss (ID). |
| Researcher Affiliation | Collaboration | Jiapeng Zhu 1 Yujun Shen 2 Yinghao Xu 3 Deli Zhao 4 Qifeng Chen 1 1Department of CSE, The Hong Kong University of Science and Technology, Hong Kong, China. 2Byte Dance, Beijing, China 3Department of IE, The Chinese University of Hong Kong, Hong Kong, China. 4Ant Research, Hangzhou, China. |
| Pseudocode | No | The paper describes the steps of the method in paragraph form (e.g., "First, we need to compute the Jacobian... Second, obtaining Jf and Jb... Third, solving Equation (8) or Equation (11)...") but does not provide a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | Our source code can be found at here. |
| Open Datasets | Yes | And the datasets we use are diverse, including FFHQ (Karras et al., 2019), LSUN bedroom, church, car (Yu et al., 2015), and Image Net (Deng et al., 2009). |
| Dataset Splits | No | The paper mentions using pre-trained models and datasets but does not explicitly provide details about train/validation/test dataset splits used for their own evaluation. |
| Hardware Specification | Yes | All the experiments are conducted on a single RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions using pre-trained models from "Tensor Flow Hub" but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | To handle such a case, we make a slight modification on JT b Jb to make it non-singular as JT b Jb JT b Jb + τtr(JT b Jb)I, (10) where I is the identity matrix and tr( ) denotes the trace. τ = 1e 3 is a small scaling factor. |