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].
G2L-CariGAN: Caricature Generation from Global Structure to Local Features
Authors: Xin Huang, Yunfeng Bai, Dong Liang, Feng Tian, Jinyuan Jia
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare our G2L-Cari GAN to state-of-the-art methods and evaluate its performance. |
| Researcher Affiliation | Academia | 1Tongji University 2Duke Kunshan University EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or a link for the open-source code of their proposed method. |
| Open Datasets | Yes | To train the style transfer module Ts, we use Webcaricature (Huo et al. 2018), which is a large unpaired photo-caricature dataset consisting of 6042 caricatures and 5974 photos from 252 persons in total. |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly provide details about validation dataset splits (e.g., percentages, sample counts, or citations to predefined validation splits). |
| Hardware Specification | Yes | We use an RTX 3060 GPU for all experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies such as programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | In Eq. 3, we set λcon = 0.01 and λsty = 20. In Eq. 9, we set λrec = 1, λc = 1, λcariid = 0.01. wr can be set to different values to control the degree of exaggeration. The learning rate, number of epochs, and batch size are as 0.01, 2000, and 1, respectively. |