Neural networks encounter the challenge of Catastrophic Forgetting (CF) in continual learning, where new task knowledge interferes with previously learned knowledge. We propose Remembering Transformer, inspired by the brain's Complementary Learning Systems (CLS), to tackle this issue. Remembering Transformer employs a mixture-of-adapters and a generative model-based routing mechanism to alleviate CF by dynamically routing task data to relevant adapters. Our approach demonstrated a new SOTA performance in various vision continual learning tasks and great parameter efficiency.

翻译：在持续学习中，神经网络面临着灾难性遗忘的挑战，即新任务知识会干扰先前学到的知识。受大脑互补学习系统的启发，我们提出记忆Transformer来解决这一问题。记忆Transformer采用混合适配器机制与基于生成模型的路由机制，通过动态地将任务数据路由至相关适配器来缓解灾难性遗忘。我们的方法在多种视觉持续学习任务中展现了新的最优性能，并具有出色的参数效率。