Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning

Understand

Meta-learning has been the most common framework for few-shot learning in recent years.

• It learns the model from collections of few-shot classification tasks, which is believed to have a key advantage of making the training objective consistent with the testing objective.
• However, some recent works report that by training for whole-classification, i.e.
• classification on the whole label-set, it can get comparable or even better embedding than many meta-learning algorithms.