零次学习是指让机器分类没见过的对象类别，开集识别要求让机器把没见过的对象类别标成“不认识”，两个任务都依赖想象能力。《反事实的零次和开集识别》提出了一种基于反事实的算法框架，通过解耦样本特征（比如对象的姿势）和类别特征（比如是否有羽毛），再基于样本特征进行反事实生成。在常用数据集上，该算法的准确率超出现有顶尖方法 2.2% 到 4.3%。论文作者岳中琪指出，AI 认知智能的进化刚刚开始，业界的探索仍处在早期阶段，今后他们将不断提升和优化相关算法。
In open-world learning, an agent starts with a set of known classes, detects, and manages things that it does not know, and learns them over time from a non-stationary stream of data. Open-world learning is related to but also distinct from a multitude of other learning problems and this paper briefly analyzes the key differences between a wide range of problems including incremental learning, generalized novelty discovery, and generalized zero-shot learning. This paper formalizes various open-world learning problems including open-world learning without labels. These open-world problems can be addressed with modifications to known elements, we present a new framework that enables agents to combine various modules for novelty-detection, novelty-characterization, incremental learning, and instance management to learn new classes from a stream of unlabeled data in an unsupervised manner, survey how to adapt a few state-of-the-art techniques to fit the framework and use them to define seven baselines for performance on the open-world learning without labels problem. We then discuss open-world learning quality and analyze how that can improve instance management. We also discuss some of the general ambiguity issues that occur in open-world learning without labels.