A Bayesian Data Augmentation Approach for Learning Deep Models

Understand

Data augmentation is an essential part of the training process applied to deep learning models.

• The motivation is that a robust training process for deep learning models depends on large annotated datasets, which are expensive to be acquired, stored and processed.
• Therefore a reasonable alternative is to be able to automatically generate new annotated training samples using a process known as data augmentation.
• The dominant data augmentation approach in the field assumes that new training samples can be obtained via random geometric or appearance transformations applied to annotated training samples, but this is a strong assumption because it is unclear if this is a reliable generative model for producing new training samples.