Meta-free representation learning for few-shot learning via stochastic weight averaging

A new transfer learning method to obtain accurate and reliable models for few-shot regression and classification is proposed, called meta-free representation learning (MFRL), which achieves the highest accuracy on a wide range of few- shot learning benchmark datasets.