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Incremental multi-domain learning with network latent tensor factorization
[article]
2019
arXiv
pre-print
The prominence of deep learning, large amount of annotated data and increasingly powerful hardware made it possible to reach remarkable performance for supervised classification tasks, in many cases saturating the training sets. However the resulting models are specialized to a single very specific task and domain. Adapting the learned classification to new domains is a hard problem due to at least three reasons: (1) the new domains and the tasks might be drastically different; (2) there might
arXiv:1904.06345v2
fatcat:5mjv6wfxubfethf3644nzqjw6i