Learning to Learn Neural Representations With Limited Data and Supervision
MCML Authors
Abstract
Abstract
Learning to learn is a powerful paradigm that enables machine learning models to leverage the previously learned features for new tasks and domains more effectively. This thesis explores different aspects of learning to learn from data, models, and semantics, and shows how they can enhance various computer vision and medical imaging tasks. In the first part of the thesis, we present novel and fundamental research on learning to learn from data, and in the second part, we investigate the use of high-level semantics in generative models.
BibTeXKey: Far24