A Conceptual Framework for Lifelong Learning
This work addresses the challenge of lifelong learning for AI systems, but it is incremental as it provides a conceptual perspective without new empirical advancements.
The paper tackles the problem of enabling machines to learn incrementally with human-like properties such as continual learning without forgetting and few-shot learning, by proposing a unified conceptual framework that supports these properties through a central mechanism, though no state-of-the-art results are presented.
Humans can learn a variety of concepts and skills incrementally over the course of their lives while exhibiting many desirable properties, such as continual learning without forgetting, forward transfer and backward transfer of knowledge, and learning a new concept or task with only a few examples. Several lines of machine learning research, such as lifelong learning, few-shot learning, and transfer learning, attempt to capture these properties. However, most previous approaches can only demonstrate subsets of these properties, often by different complex mechanisms. In this work, we propose a simple yet powerful unified framework that supports almost all of these properties and approaches through one central mechanism. We also draw connections between many peculiarities of human learning (such as memory loss and "rain man") and our framework. While we do not present any state-of-the-art results, we hope that this conceptual framework provides a novel perspective on existing work and proposes many new research directions.