  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 paper, we present a
comprehensive review of research towards 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.
