In real-world recognition/classification tasks, limited by various objective factors, it is usually difficult to collect training samples to exhaust all classes when training a recognizer or classifier. A more realistic scenario is open set recognition (OSR),where incomplete knowledge of the world exists at training time, and unknown classes can be submitted to an algorithm during testing, requiring the classifiers to not only accurately classify the seen classes, but also effectively deal with unseen ones.
to familiarize yourself with several OSR approaches read: https://arxiv.org/pdf/1811.08581.pdf (Recent Advances in Open Set Recognition: A Survey)