Abstract
Artificial intelligence (AI) and neuroscience share a rich history, with advancements in neuroscience shaping the development of AI systems capable of human-like knowledge retention. Leveraging insights from neuroscience and existing research in adversarial and continual learning, we introduce a novel framework comprising two core concepts: feature distillation and re-consolidation. Our framework, named Robust Rehearsal, addresses the challenge of catastrophic forgetting inherent in continual learning (CL) systems by distilling and rehearsing robust features. Inspired by the mammalian brain's memory consolidation process, Robust Rehearsal aims to emulate the rehearsal of distilled experiences during learning tasks. Additionally, it mimics memory re-consolidation, where new experiences influence the integration of past experiences to mitigate forgetting. Extensive experiments conducted on CIFAR10, CIFAR100, and real-world helicopter attitude datasets showcase the superior performance of CL models trained with Robust Rehearsal compared to baseline methods. Furthermore, examining different optimization training objectives-joint, continual, and adversarial learning-we highlight the crucial role of feature learning in model performance. This underscores the significance of rehearsing CL-robust samples in mitigating catastrophic forgetting. In conclusion, aligning CL approaches with neuroscience insights offers promising solutions to the challenge of catastrophic forgetting, paving the way for more robust and human-like AI systems.
Abstract (translated)
人工智能（AI）和神经科学之间有一个丰富的历史，神经科学的进步塑造了能够像人类一样保留知识的人工智能系统的发展。利用神经科学和现有对抗学习和持续学习的研究，我们引入了一个新框架，包括两个核心概念：特征提取和重新巩固。我们的框架名为Robust Rehearsal，通过提取和演练 robust 特征来解决连续学习（CL）系统中的灾难性遗忘挑战。灵感来自哺乳动物大脑的记忆巩固过程，Robust Rehearsal 旨在模仿在学习任务中的提取经历的演练。此外，它还模仿了记忆重新巩固，即新的经验通过整合过去的经验来缓解遗忘。在CIFAR10、CIFAR100和现实直升机的姿态数据集上进行的大量实验展示了使用Robust Rehearsal训练的CL模型的优越性能，相对于基线方法。此外，通过研究不同优化训练目标——联合、持续和对抗学习——我们强调了特征学习在模型性能中的关键作用。这使得在减轻灾难性遗忘方面演习 CL-robust 样本具有重要意义。总之，将AI方法与神经科学见解相结合为解决灾难性遗忘提供了解决方案，为更加健壮和像人类一样的AI系统铺平道路。
URL
https://arxiv.org/abs/2404.14588
