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Online Continual Learning for Embedded Devices. (arXiv:2203.10681v3 [cs.LG] UPDATED)
cs.LG updates on arXiv.org arxiv.org
Real-time on-device continual learning is needed for new applications such as
home robots, user personalization on smartphones, and augmented/virtual reality
headsets. However, this setting poses unique challenges: embedded devices have
limited memory and compute capacity and conventional machine learning models
suffer from catastrophic forgetting when updated on non-stationary data
streams. While several online continual learning models have been developed,
their effectiveness for embedded applications has not been rigorously studied.
In this paper, we first identify criteria that online continual learners …
arxiv continual devices embedded embedded devices learning lg