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Journal of Imaging, Volume 8, Issue 4
April 2022 - 36 articles
Cover Story: Modern deep neural networks are well known to be weak in the face of unknown data instances. Avoiding false predictions by identifying substantially different data from what has been seen during training remains a challenge. Although it is inevitable for continual-learning systems to encounter unseen concepts, the corresponding literature primarily focuses on preventing the catastrophic forgetting of learned representations. We bridge this gap by introducing the open variational auto-encoder (OpenVAE). OpenVAE unifies the detection of unseen, unknown, out-of-distribution data and the preservation of already acquired knowledge in continual training for robust application. View this paper.
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J. Imaging - ISSN 2313-433XCreative Common CC BY license