Reinforced Continual Learning
This work addresses the challenge of enabling AI models to learn new tasks efficiently without forgetting previous knowledge, which is an incremental improvement in the continual learning domain.
The paper tackles the problem of catastrophic forgetting in continual learning by proposing a reinforcement learning-based method to search for optimal neural architectures for each new task, achieving superior performance on sequential classification tasks with MNIST and CIFAR-100 variants compared to existing methods.
Most artificial intelligence models have limiting ability to solve new tasks faster, without forgetting previously acquired knowledge. The recently emerging paradigm of continual learning aims to solve this issue, in which the model learns various tasks in a sequential fashion. In this work, a novel approach for continual learning is proposed, which searches for the best neural architecture for each coming task via sophisticatedly designed reinforcement learning strategies. We name it as Reinforced Continual Learning. Our method not only has good performance on preventing catastrophic forgetting but also fits new tasks well. The experiments on sequential classification tasks for variants of MNIST and CIFAR-100 datasets demonstrate that the proposed approach outperforms existing continual learning alternatives for deep networks.