Budget-Aware Adapters for Multi-Domain Learning

@article{Berriel2019BudgetAwareAF,
  title={Budget-Aware Adapters for Multi-Domain Learning},
  author={R. Berriel and St{\'e}phane Lathuili{\`e}re and Moin Nabi and T. Klein and Thiago Oliveira-Santos and N. Sebe and E. Ricci},
  journal={2019 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2019},
  pages={382-391}
}
  • R. Berriel, Stéphane Lathuilière, +4 authors E. Ricci
  • Published 2019
  • Computer Science
  • 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
  • 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. [...] Key Method To implement this idea we derive specialized deep models for each domain by adapting a pre-trained architecture but, differently from other methods, we propose a novel strategy to automatically adjust the computational complexity of the network.Expand Abstract

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