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We introduce a novel method of generating synthetic question answering corpora by combining models of question generation and answer extraction, and by filtering the results to ensure roundtrip consistency.
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.
The models are placed in physically realistic poses with respect to their environment to generate a labeled synthetic dataset.
By shedding light on the promise and challenges, we hope our work can rekindle the conversation on workflows for data sharing.
Our model, which we call HP-GAN, learns a probability density function of future human poses conditioned on previous poses.
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.
Ranked #1 on Synthetic Data Generation on UCI Epileptic Seizure Recognition (using extra training data)