Adaptive Augmentation

Adaptive augmentation in machine learning aims to dynamically adjust data augmentation strategies during training, improving model performance and generalization compared to static methods. Current research focuses on developing algorithms that learn optimal augmentation parameters using reinforcement learning, bilevel optimization, or by selectively augmenting specific features or samples based on model feedback. This adaptive approach addresses limitations of traditional augmentation techniques, leading to more robust and efficient training, particularly beneficial in scenarios with limited data or complex data types, and impacting various fields including medical image analysis and natural language processing.