Semantic Augmentation in Images using Language
Sahiti Yerramilli Jayant Sravan Tamarapalli Tanmay Girish Kulkarni Jonathan Francis Eric Nyberg
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.