You can find below our recently accepted work in the prestigious Springer Artificial Intelligence Review (5-year journal impact factor: 11.7), entitled “Advances in diffusion models for image data augmentation: a review of methods, models, evaluation metrics and future research directions” and authored by P. Alimisis, I. Mademlis, P. Radoglou-Grammatikis, P. Sarigiannidis and G. Th. Papadopoulos.
Link: here
Image data augmentation constitutes a critical methodology in modern computer vision tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; thereby, improving the performance and robustness of machine learning models in downstream tasks. On the other hand, Diffusion Models (DMs), which comprise one of the most recent and highly promising classes of methods in the field of generative Artificial Intelligence (AI), have emerged as a powerful tool for image data augmentation, capable of generating realistic and diverse images by learning the underlying data distribution.
The current study realizes a systematic, comprehensive and in-depth review of DM-based approaches for image augmentation, covering a wide range of strategies, tasks and applications. In particular, the work focuses on the following key aspects:
- A comprehensive analysis of the fundamental principles, model architectures and training strategies of DMs is performed.
- A taxonomy of the relevant image augmentation methods is introduced, focusing on techniques regarding semantic manipulation, personalization and adaptation, and application-specific augmentation tasks.
- Performance assessment methodologies and respective evaluation metrics are analyzed.
- Current challenges and future research directions in the field are discussed.
