Cold Start Streaming Learning for Deep Networks. (arXiv:2211.04624v1 [cs.LG])

The ability to dynamically adapt neural networks to newly-available data
without performance deterioration would revolutionize deep learning
applications. Streaming learning (i.e., learning from one data example at a
time) has the potential to enable such real-time adaptation, but current
approaches i) freeze a majority of network parameters during streaming and ii)
are dependent upon offline, base initialization procedures over large subsets
of data, which damages performance and limits applicability. To mitigate these
shortcomings, we propose Cold Start Streaming Learning (CSSL), …

arxiv cold start networks streaming