Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
High-Resolution GAN Inversion for Degraded Images in Large Diverse Datasets
Authors: Yanbo Wang, Chuming Lin, Donghao Luo, Ying Tai, Zhizhong Zhang, Yuan Xie
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present comprehensive experiments to evaluate our method. We experiment with image inpainting, image colorization and super-resolution. ... We conduct extensive experiments on restoration tasks with different datasets and GAN models. The experimental results demonstrate the superiority of our method against other inversion methods. |
| Researcher Affiliation | Collaboration | Yanbo Wang1*, Chuming Lin2, Donghao Luo2, Ying Tai2, Zhizhong Zhang1 , Yuan Xie1 1School of Computer Science and Technology, East China Normal University 2Tencent Youtu Lab |
| Pseudocode | Yes | Algorithm 1 Pseudocode of CRI |
| Open Source Code | Yes | Code is available at https://github.com/Booooooooooo/CRI. |
| Open Datasets | Yes | Image Net (Krizhevsky, Sutskever, and Hinton 2012) ... FFHQ (Karras, Laine, and Aila 2019) ... Celeb A-HQ (Karras et al. 2017) ... BSD100 (Martin et al. 2001) dataset and DIV2K (Agustsson and Timofte 2017) dataset. |
| Dataset Splits | Yes | We invert 1000 images from the validation set of Image Net, each from different classes, to quantitatively evaluate the methods. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) are mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4). |
| Experiment Setup | Yes | For Style GAN-XL with Image Net, we use 1000 iterations for the optimization stage and use 350 iterations for the finetune stage as in (Sauer, Schwarz, and Geiger 2022). For Style GAN2 with FFHQ, we use 500 iterations for the first stage and 20 iterations for the second stage as inversion with a face is simpler. |