Core Concepts
Automated machine learning (AutoML) principles are utilized to enhance data augmentation techniques, leading to improved performance over traditional methods.
Abstract
Data augmentation is crucial for enhancing machine learning models. Automated approaches outperform classical methods by utilizing AutoML principles to optimize augmentation strategies.
Reviewing the content reveals that data augmentation is a vital technique in machine learning. The use of automated methods based on AutoML principles has shown superior performance compared to traditional approaches. These automated techniques aim to optimize augmentation strategies efficiently.
Stats
"The results show that AutoML methods for data augmentation currently outperform state-of-the-art techniques based on conventional approaches."
"Generative modeling techniques such as VAEs and GANs have shown promise in generating synthetic data to alleviate data problems but they also suffer from overfitting when trained on insufficient data."
Quotes
"The most commonly used data augmentation techniques include geometric transformations – particularly, rotation, flipping, shearing and scaling–and photometric transformations such as color jittering, solarizaion, brightness and contrast adjustment."
"Approaches based on GANs are also not guaranteed to produce good results even in cases where sufficiently large and rich datasets are available."