Deep Learning models are incredibly data-hungry and require very large labeled datasets for supervised learning. As a consequence, these models often suffer from overfitting, limiting their ability to generalize to real-world examples. Recent advancements in diffusion models have enabled the generation of photorealistic images based on textual inputs. Leveraging the substantial datasets used to train these diffusion models, we propose a technique to utilize generated images to augment existing datasets. This paper explores various strategies for effective data augmentation to improve the out-of-domain generalization capabilities of deep learning models.
@article{arxiv.2404.02353,
title = {Semantic Augmentation in Images using Language},
author = {Sahiti Yerramilli and Jayant Sravan Tamarapalli and Tanmay Girish Kulkarni and Jonathan Francis and Eric Nyberg},
journal= {arXiv preprint arXiv:2404.02353},
year = {2025}
}