Transformation-Based Data Synthesis for Limited Sample Scenario

Transformation-Based Data Synthesis for Limited Sample Scenario

Blog Article

We consider a challenging learning scenario where neither pretext training nor auxiliary data are available except for small training samples.We call this a transfer-free scenario where we cannot access any transferable knowledge or data.Our proposal for resolving this issue is to learn a pair-wise transformation function (e.

g., spatial or appearance) between given samples.This simple setting yields two Clay Mask practical advantages.

The training objective can be defined as a simple reconstruction loss, and data can be synthesized by merely manipulating or sampling the learned transformations.However, the limitation of previous transformation methods lies in a strong assumption that all images should be transformable to each other, i.e.

, all-to-all transformable.To relax this constraint, we propose a novel concept called ‘template,’ designed to be transformable to any other data, i.e.

, “template-to-all” transformable.A range of experiments on the transfer-free scenarios confirms that our model successfully learns transformation and synthesizes new data from minimal Vitamin D training data (less than five or ten for each class).The subsequent data augmentation experiments showed significantly improved classification performance.