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Found 2 papers, 1 papers with code
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning
In this work we critically survey the literature and argue that notable lessons from open set recognition, identifying unknown examples outside of the observed set, and the adjacent field of active learning, querying data to maximize the expected performance gain, are frequently overlooked in the deep learning era.
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
Modern deep neural networks are well known to be brittle in the face of unknown data instances and recognition of the latter remains a challenge.
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