A Comprehensive Survey on Data Augmentation

arXiv:2405.09591v1 Announce Type: new
Abstract: Data augmentation is a series of techniques that generate high-quality artificial data by manipulating existing data samples. By leveraging data augmentation techniques, AI models can achieve significantly improved applicability in tasks involving scarce or imbalanced datasets, thereby substantially enhancing AI models' generalization capabilities. Existing literature surveys only focus on a certain type of specific modality data, and categorize these methods from modality-specific and operation-centric perspectives, which lacks a consistent summary of data augmentation methods across …

abstract ai models artificial arxiv augmentation capabilities cs.ai cs.lg data datasets focus generate leveraging data literature quality samples series survey surveys tasks type