Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions
<|reference_start|>Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions: 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. In parallel, augmentation approaches can also be used for editing/modifying a given image in a context- and semantics-aware way. 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, a comprehensive analysis of the fundamental principles, model architectures and training strategies of DMs is initially performed. Subsequently, a taxonomy of the relevant image augmentation methods is introduced, focusing on techniques regarding semantic manipulation, personalization and adaptation, and application-specific augmentation tasks. Then, performance assessment methodologies and respective evaluation metrics are analyzed. Finally, current challenges and future research directions in the field are discussed.<|reference_end|>
@article{alimisis2024advances,
    title={Advances in Diffusion Models for Image Data Augmentation: A Review of
  Methods, Models, Evaluation Metrics and Future Research Directions},
    author={Panagiotis Alimisis, Ioannis Mademlis, Panagiotis
  Radoglou-Grammatikis, Panagiotis Sarigiannidis, Georgios Th. Papadopoulos},
    journal={arXiv preprint arXiv:2407.04103},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04103},
    primaryClass={cs.CV cs.AI}
}