Web: http://arxiv.org/abs/2108.02671

May 6, 2022, 1:12 a.m. | Julia Hornauer, Lazaros Nalpantidis, Vasileios Belagiannis

cs.LG updates on arXiv.org arxiv.org

Real-world perception systems in many cases build on hardware with limited
resources to adhere to cost and power limitations of their carrying system.
Deploying deep neural networks on resource-constrained hardware became possible
with model compression techniques, as well as efficient and hardware-aware
architecture design. However, model adaptation is additionally required due to
the diverse operation environments. In this work, we address the problem of
training deep neural networks on resource-constrained hardware in the context
of visual domain adaptation. We select …

arxiv cv domain adaptation hardware on

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