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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |