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Budget-Aware Adapters for Multi-Domain Learning
[article]
2019
arXiv
pre-print
Multi-Domain Learning (MDL) refers to the problem of learning a set of models derived from a common deep architecture, each one specialized to perform a task in a certain domain (e.g., photos, sketches, paintings). This paper tackles MDL with a particular interest in obtaining domain-specific models with an adjustable budget in terms of the number of network parameters and computational complexity. Our intuition is that, as in real applications the number of domains and tasks can be very large,
arXiv:1905.06242v2
fatcat:ao2g3vo6ibb4xfaw463flnxnam