深度学习模型面对非独立同分布数据流时, 新知识会覆盖旧知识, 造成其性能大幅度下降.
而持续学习(CL) 技术可以从非独立同分布数据流中获取增量可用知识, 不断积累新知识的同时 …

Deep learning model facing the non-independent and identically distributed data streams,
the old knowledge will be covered by new knowledge, resulting in a significant performance …

摘要近年来, 随着信息技术地不断发展, 各种数据呈现爆炸式地增长, 传统的机器学习算法只有当
测试数据与训练数据分布类似时, 学习算法才能取得较好的性能, 换句话说 …

Continual Learning (CL) focuses on learning from dynamic and changing data distributions
while retaining previously acquired knowledge. Various methods have been developed to …

Deep learning, trained primarily on a single task under the assumption of independent and
identically distributed (iid) data, has made enormous progress in recent years. However …

无监督持续学习(UCL) 是指能够随着时间的推移而学习, 同时在没有监督的情况下记住以前的
模式. 虽然在这个方向上取得了很大进展, 但现有工作通常假设对于即将到来的数据有强大的先 …

Continual learning (CL) over non-stationary data streams remains one of the long-standing
challenges in deep neural networks (DNNs) as they are prone to catastrophic forgetting. CL …

Continual learning (CL) is a major challenge of machine learning (ML) and describes the
ability to learn several tasks sequentially without catastrophic forgetting (CF). Recent works …

Continual learning (CL), or the ability to acquire, process, and learn from new information
without forgetting acquired knowledge, is a fundamental quality of an intelligent agent. The …

One of the challenges of current machine learning systems is that standard AI paradigms are
not good at transferring (or leveraging) knowledge across tasks. While many systems have …