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ShareScore release 0.9.0
Dataset results
2 results for “IJCAI 2019”
Dataset for IJCAI 2019 paper, Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks
<p>The dataset and pre-trained model for IJCAI 2019 paper "Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks"</p> <ul> <li>Pre-trained model(Pytorch): DSPNet_G_200_epochs.pth</li> <li>Dataset (generated from coco dataset): starry_night_coco.zip</li> <li>Github: https://github.com/jinningli/DSP-Net</li> </ul> <p>Please cite our paper if you are using this dataset:</p> <p><em>Jinning Li, and Yexiang Xue. Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks. In International Joint Conference on Artificial Intelligence (IJCAI) 2019</em></p> <p>Abstract:</p> <p><em>We propose the Dual Scribble-to-Painting Network (DSP-Net), which is able to produce artistic paintings based on user-generated scribbles. In scribble-to-painting transformation, a neural net has to infer additional details of the image, given relatively sparse information contained in the outlines of the scribble. Therefore, it is more challenging than classical image style transfer, in which the information content is reduced from photos to paintings. Inspired by the human cognitive process, we propose a multi-task generative adversarial network, which consists of two jointly trained neural nets -- one for generating artistic images and the other one for semantic segmentation. We demonstrate that joint training on these two tasks brings in additional benefit. Experimental result shows that DSP-Net outperforms state-of-the-art models both visually and quantitatively. In addition, we publish a large dataset for scribble-to-painting transformation.</em></p>
Experimental data from the IJCAI 2019 paper "Pattern Selection for Optimal Classical Planning with Saturated Cost Partitioning"
<p>This data set contains the raw experiment data, parsed values and basic reports for the experiments in the paper. For each experiment there are two directories. The first directory contains the raw data of all experiment runs. The code directories and benchmark files have been removed to avoid duplication and to save space. The second directory (*-eval) contains a "properties" file with all parsed values and an HTML report.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.