Block Neural Network Avoids Catastrophic Forgetting When Learning Multiple Task
Laboratoire Psychologie de la Perception（感知心理学实验室）；Université Paris Descartes（巴黎笛卡尔大学）
英文摘要：
In the present work we propose a Deep Feed Forward network architecture which can be trained according to a sequential learning paradigm, where tasks of increasing difficulty are learned sequentially, yet avoiding catastrophic forgetting. The proposed architecture can re-use the features learned on previous tasks in a new task when the old tasks and the new one are related. The architecture needs fewer computational resources (neurons and connections) and less data for learning the new task than a network trained from scratch
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
