Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks
Authors: Yabo Zhang, mingshuai Yao, Yuxiang Wei, Zhilong Ji, Jinfeng Bai, Wangmeng Zuo
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our method outperforms the state-of-the-arts both quantitatively and qualitatively, especially for the cases of large domain gaps. Moreover, our Di Fa can easily be extended to zero-shot generative domain adaption with appealing results. |
| Researcher Affiliation | Collaboration | 1Harbin Institute of Technology 2Tomorrow Advancing Life 3Peng Cheng Laboratory |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https: //github.com/YBYBZhang/Di Fa. |
| Open Datasets | Yes | For FFHQ adaption, the target images are collected from three datasets: (i) Artstation Artistic-face-HQ (AAHQ) [17], (ii) Met Faces [10], and (iii) face paintings by Amedeo Modigliani, Fernand Leger and Raphael [36]. For Cat adaption, we collect target images from the AFHQ-Wild validation dataset and divide them into Tiger, Fox, and Wolf datasets. We use AFHQ, Met Faces, and Artistic-Faces datasets during training, and their license are included in our repository https://github.com/YBYBZhang/Di Fa. |
| Dataset Splits | No | The paper mentions calculating metrics against "validation sets" for evaluation, but does not provide specific train/validation/test splits for the datasets used in their model's training process. |
| Hardware Specification | Yes | We finetune the generator for 300 400 iterations, which takes about 3 4 minutes on an RTX 2080Ti GPU. |
| Software Dependencies | No | The paper mentions using StyleGAN2, StyleGAN-ADA, e4e, pSp, CLIP, and ADAM optimizer, but does not provide specific version numbers for any of these software components or libraries. |
| Experiment Setup | Yes | For training, we use ADAM optimizer [13] with a learning rate 0.02 and set the batch size to 2. We finetune the generator for 300 400 iterations. Our overall training loss consists of three terms, i.e., the global-level adaption loss Lglobal, the attentive style loss Llocal and the selective cross-domain consistency loss Lscc. In our experiments, we use λlocal = 2 and λscc = max(0, niter n B Niter niter ). |