ReGANIE: Rectifying GAN Inversion Errors for Accurate Real Image Editing
Authors: Bingchuan Li, Tianxiang Ma, Peng Zhang, Miao Hua, Wei Liu, Qian He, Zili Yi
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our approach yields near-perfect reconstructions without sacrificing the editability, thus allowing accurate manipulation of real images. Further, we evaluate the performance of our rectifying network, and see great generalizability towards unseen manipulation types and out-of-domain images. |
| Researcher Affiliation | Industry | Bingchuan Li, Tianxiang Ma, Peng Zhang, Miao Hua, Wei Liu, Qian He, Zili Yi* Byte Dance Ltd, Beijing, China {libingchuan, matianxiang.724, liuwei.jikun, zhangpeng.ucas, heqian, huamiao, yizili}@bytedance.com |
| Pseudocode | Yes | Algorithm 1: Training & inference |
| Open Source Code | No | The paper does not contain an explicit statement about releasing the source code or provide a link to a code repository for the methodology described. |
| Open Datasets | Yes | To demonstrate the generalizability of our method on unseen realistic faces, we train our model on the FFHQ (Karras, Laine, and Aila 2019) dataset, and test on the Celeb A-HQ (Karras et al. 2018) dataset. We also experiment our method on the animal portrait dataset (Choi et al. 2020) and the anime dataset (Branwen 2019). |
| Dataset Splits | No | The paper mentions training on FFHQ and testing on Celeb A-HQ but does not provide specific train/validation/test dataset split percentages or sample counts. |
| Hardware Specification | Yes | The proposed Re GANIE network is trained on a single Tesla V100 GPU with batch size of 8 on 512 512 resolution images. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies, such as programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | The proposed Re GANIE network is trained on a single Tesla V100 GPU with batch size of 8 on 512 512 resolution images. It is optimized using the Adam optimizer (Kingma and Ba 2014) with b1 = 0.0 and b2 = 0.99, and the learning rate is fixed at 0.002. In all experiments, the training is performed for 100,000 iterations. |