A Comprehensive Survey on Data Augmentation
It addresses the problem of fragmented understanding in data augmentation for researchers and practitioners, though it is incremental as it builds on existing surveys.
This survey tackles the lack of a consistent summary of data augmentation methods across multiple modalities by proposing a more enlightening taxonomy that categorizes techniques for five common data modalities using a unified inductive approach.
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 multiple modalities and limits the comprehension of how existing data samples serve the data augmentation process. To bridge this gap, this survey proposes a more enlightening taxonomy that encompasses data augmentation techniques for different common data modalities by investigating how to take advantage of the intrinsic relationship between and within instances. Additionally, it categorizes data augmentation methods across five data modalities through a unified inductive approach.