Semantic Augmentation in Images using Language
<|reference_start|>Semantic Augmentation in Images using Language: 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.<|reference_end|>
@article{yerramilli2024semantic,
    title={Semantic Augmentation in Images using Language},
    author={Sahiti Yerramilli, Jayant Sravan Tamarapalli, Tanmay Girish Kulkarni,
  Jonathan Francis, Eric Nyberg},
    journal={arXiv preprint arXiv:2404.02353},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02353},
    primaryClass={cs.CV cs.AI cs.LG}
}