To achieve generalizable Deep Learning models, large amounts
of data are needed. Sometimes, data may be scarce and expensive to
collect. Standard augmentation techniques such as
flipping, blurring or random rotation are routinely performed to help boosting model performance but these techniques have its own limitations and pitfalls.
In this session, we will explore how to augment training datasets using Stable Diffusion for a domain-specific use case.