Georgia Tech researchers Vidya Muthukumar and Eva Dyer are leading a multi-institutional project to develop a theory for data augmentation, aiming to improve the generalization and fairness of AI ...

Currently, deep learning is the most important technique for solving many complex machine vision problems. State-of-the-art deep learning models typically contain a very large number of parameters ...

A conditional generative adversarial network architecture was implemented to generate synthetic data. Use cases were myelodysplastic syndromes (MDS) and AML: 7,133 patients were included. A fully ...

The AI revolution that we’re currently living through is a direct result of the explosion in the amount of data that’s available to be mined and analyzed for insights. However, collecting data from ...

Machine learning (ML) has emerged as a promising tool for tackling challenges in aquatic environmental research, especially ...

Synthetic data generation (SDG) was proposed in the early nineties as a form of imputation. 1 Since then, multiple statistical and machine learning (ML) methods have been developed to generate ...

A new technical paper titled “An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design” was published by researchers at the University of Texas at Austin, Nvidia ...

You’ve just finished a strenuous hike to the top of a mountain. You’re exhausted but elated. The view of the city below is gorgeous, and you want to capture the moment on camera. But it’s already ...