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A Unified Perspective on Multi-Domain and Multi-Task Learning
[article]
2015
arXiv
pre-print
In this paper, we provide a new neural-network based perspective on multi-task learning (MTL) and multi-domain learning (MDL). By introducing the concept of a semantic descriptor, this framework unifies MDL and MTL as well as encompassing various classic and recent MTL/MDL algorithms by interpreting them as different ways of constructing semantic descriptors. Our interpretation provides an alternative pipeline for zero-shot learning (ZSL), where a model for a novel class can be constructed
arXiv:1412.7489v3
fatcat:uzugz3hta5ei3kxy4g5szbjhue