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..
Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks
Authors: Jinning Li, Yexiang Xue
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental result shows that DSP-Net outperforms state-of-the-art models both visually and quantitatively. |
| Researcher Affiliation | Academia | Jinning Li1 and Yexiang Xue2 1Shanghai Jiao Tong University 2Purdue University EMAIL, EMAIL |
| Pseudocode | No | The paper describes the network architecture and mathematical objectives, but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and dataset could be found at https://github.com/jinningli/DSP-Net. |
| Open Datasets | Yes | In this paper, we build a triple dataset including scribbles, paintings and semantic images based on COCO dataset [Lin et al., 2014]. |
| Dataset Splits | No | We split the dataset into 4500 images for training and 500 images for a test. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., GPU models, CPU types, or cloud resources). |
| Software Dependencies | No | The paper mentions various models and algorithms used (e.g., VGG19 network, Pix2pix, Cycle GAN), but it does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We train the GAN based models for 200 epochs and neural style for 1000 iterations. We keep the other hyper-parameters and basic settings as the recommended value mentioned in their original code. |