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Current deep learning methods are regarded as favorable if they empiricallyperform well on dedicated test sets. This mentality is seamlessly reflected inthe resurfacing area of continual learning, where consecutively arriving datais investigated. The core challenge is framed as protecting previously acquiredrepresentations from being catastrophically forgotten. However, comparison ofindividual methods is nevertheless performed in isolation from the real worldby monitoring accumulated benchmark test set performance. The closed worldassumption remains predominant, i.e. models are evaluated on data that isguaranteed to originate from the same distribution as used for training. Thisposes a massive challenge as neural networks are well known to provideoverconfident false predictions on unknown and corrupted instances. In thiswork we critically survey the literature and argue that notable lessons fromopen set recognition, identifying unknown examples outside of the observed set,and the adjacent field of active learning, querying data to maximize theexpected performance gain, are frequently overlooked in the deep learning era.Hence, we propose a consolidated view to bridge continual learning, activelearning and open set recognition in deep neural networks. Finally, theestablished synergies are supported empirically, showing joint improvement inalleviating catastrophic forgetting, querying data, selecting task orders,while exhibiting robust open world application.

A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning