变化环境自适应感知与学习研究进展

Research Advances on Adaptive Perception and Learning in Changing Environment

人工智能的研究问题现已逐步从封闭环境扩展到开放环境,但开放环境中存在各种变化性因素,导致传统基于封闭世界假设和独立同分布假设的学习模型与算法性能明显下降.因此,变化环境下的自适应感知与学习成为当前人工智能研究的前沿热点,文中聚焦其中三个方面介绍相关最新研究进展.针对类别集变化,介绍开放集识别与分布外样本检测、新类别自主发现、类别增量学习等问题;针对数据分布变化,介绍领域自适应、领域泛化、测试时自适应等问题;针对数据质量变化,介绍弱监督学习和标签噪声学习等问题.最后分析未来的研究趋势.

The research on artificial intelligence is gradually extended to open environment from closed environment.There are various changing factors in open environment leading to evident performance degradation of the traditional models and learning algorithms based on closed set assumption and independently and identically distributed assumption.Therefore,adaptive perception and learning in changing environments is a frontier topic in the field of artificial intelligence.The latest advances are introduced from three aspects.For category changing,research issues of open set recognition and out-of-distribution detection,new categories discovery and class-incremental learning are introduced.For data distribution changing,issues of domain adaptation,domain generalization and test-time adaptation are introduced.For data quality changing,issues of weakly supervised learning and label noise learning are introduced.Finally,future research trends are analyzed and discussed.