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This paper proposes a deep neural network structure that exploits edge information in addressing representative low-level vision tasks such as layer separation and image filtering.
Gathering and annotating that sheer amount of data in the real world is a time-consuming and error-prone task.
DoppelGANger is designed to work on time series datasets with both continuous features (e. g. traffic measurements) and discrete ones (e. g., protocol name).
Research on style transfer and domain translation has clearly demonstrated the ability of deep learning-based algorithms to manipulate images in terms of artistic style.
To demonstrate the model fidelity, we show that CorGAN generates synthetic data with performance similar to that of real data in various Machine Learning settings such as classification and prediction.
SOTA for Synthetic Data Generation on UCI Epileptic Seizure Recognition (using extra training data)
In this work we introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversarial Networks (GANs).
While automatic text extraction works well on infographics, computer vision approaches trained on natural images fail to identify the stand-alone visual elements in infographics, or `icons'.