Data augmentation encompasses a diverse array of methods designed to expand and diversify training datasets without the need for additional manual annotation. In computer vision, common approaches ...

The SeisAug toolkit addresses a significant challenge in seismological studies: the limited availability of region- and depth-specific labeled data. This scarcity poses a preliminary drawback when ...

Achieving high reliability in AI systems—such as autonomous vehicles that stay on course even in snowstorms or medical AI that can diagnose cancer from low-resolution images—depends heavily on model ...

In marine ecology research, it is crucial to accurately identify the marine mammal species active in the target area during the current season, which helps researchers understand the behavioral ...

A Python library for advanced and novel data augmentation, combining traditional techniques like cropping and blurring with state-of-the-art generative AI methods such as style transfer, image ...

New research from the Data Provenance Initiative has found a dramatic drop in content made available to the collections used to build artificial intelligence. By Kevin Roose Reporting from San ...

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 ...

Abstract: The accuracy obtained with deep learning-based systems usually depends on the availability of large image datasets, which is not always possible. Consequently, it is necessary to apply ...