The accuracies for many pattern recognition tasks have increased rapidly year by year, achieving or even outperforming human performance. From the perspective of accuracy, pattern recognition seems to be a nearly solved problem. However, once launched in real applications, the high-accuracy pattern recognition systems may become unstable and unreliable due to the lack of robustness in open and changing environments. In this article, we present a comprehensive review of research toward robust pattern recognition from the perspective of breaking three basic and implicit assumptions: closed-world assumption, independent and identically distributed assumption, and clean and big data assumption, which form the foundation of most pattern recognition models. Actually, our brain is robust at learning concepts continually and incrementally, in complex, open, and changing environments, with different contexts, modalities, and tasks, by showing only a few examples, under weak or noisy supervision. These are the major differences between human intelligence and machine intelligence, which are closely related to the above three assumptions. After witnessing the significant progress in accuracy improvement nowadays, this review paper will enable us to analyze the shortcomings and limitations of current methods and identify future research directions for robust pattern recognition.

中文翻译：

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走向稳健的模式识别：回顾

许多模式识别任务的准确率逐年快速提高，达到甚至超过人类的表现。从准确性的角度来看，模式识别似乎是一个几乎可以解决的问题。然而，一旦在实际应用中推出，由于在开放和变化的环境中缺乏鲁棒性，高精度模式识别系统可能会变得不稳定和不可靠。在本文中，我们从打破三个基本和隐含假设的角度对鲁棒模式识别的研究进行了全面回顾：封闭世界假设、独立同分布假设以及清洁和大数据假设，它们构成了大多数研究的基础。模式识别模型。实际上，我们的大脑在不断地、渐进地、在复杂的、开放和不断变化的环境，具有不同的背景、方式和任务，只展示几个例子，在弱或嘈杂的监督下。这些是人类智能和机器智能的主要区别，这与上述三个假设密切相关。在见证了当今精度提高的重大进展后，这篇综述论文将使我们能够分析当前方法的缺点和局限性，并确定鲁棒模式识别的未来研究方向。