Fast adaptation of neural networks

Abstract

The ability to learn quickly from a few samples is a vital element of intelligence. Humans can reuse past knowledge and learn incredibly quickly. Also humans are able to interact with others to effectively guide their learning process. Computer vision systems for recognizing objects automatically from pixels are becoming commonplace in production systems. These modern computer vision systems use deep neural networks to automatically learn and recognize objects from data. Oftentimes, these deep neural networks used in production require a lot of data, take a long time to learn and forget old things when learning something new.

We build upon previous methods called Prototypical Networks and Model-Agnostic Meta-Learning (MAML) that enables machines to learn to recognize new objects with very little supervision from the user. We extend these methods to the semi-supervised few-shot learning scenario, where the few labeled samples are accompanied with (potentially many) unlabeled samples. Our proposed methods are able to learn better by also making use of the additional unlabeled samples. We note that in many real-world applications the adaptation performance can be significantly improved by requesting the few labels through user feedback (active adaptation). Further, our proposed methods can also adapt to new tasks without any labeled examples (unsupervised adaptation) when the new task has the same output space as the training tasks do.