Semantic Augmentation in Images using Language

Understand

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.