In-memory physical superposition meets few-shot continual learning

Deep convolutional neural networks (CNNs) have achieved remarkable success in computer vision tasks, like image classification. This is the result of the availability of a large amount of training samples, as well as being able to leverage huge computational and memory resources.

This, however, poses challenges for their applicability to standalone smart agents deployed in new and dynamic environments. In these cases, there is a need for agents to continually learn about novel classes they encounter from very few training …

continual memory superposition