Channel Data Augmentation

Channel data augmentation enhances deep learning models by artificially increasing training data diversity through modifications applied to input channels (e.g., spectral bands in images, microphone signals in audio). Current research focuses on developing augmentation strategies tailored to specific data types and tasks, often integrating these techniques within semi-supervised or self-supervised learning frameworks, including contrastive learning and teacher-student models. This approach addresses the limitations of limited labeled data, improving model robustness and generalization, particularly in applications like 3D object detection, indoor localization, and remote sensing image classification where data acquisition is costly or challenging.