Meta-learning systems can be trained to achieve a large number of tasks and are then tested for their ability to learn new tasks. A famous example of this kind of meta-learning is the so-called Transfer Learning discussed in the Chapter on Advanced CNNs, where networks can successfully learn new image-based tasks from relatively small datasets. However, there is no analogous pre-training scheme for non-vision domains such as speech, language, and text.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, Chelsea Finn, Pieter Abbeel, Sergey Levine, 2017, https://arxiv.org/abs/1703.03400 proposes a model-agnostic approach names MAML, compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is...
