Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights

ECCV, pp. 72-88, 2018.

Cited by: 137|Bibtex|Views65
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

This work presents a method for adapting a single, fixed deep neural network to multiple tasks without affecting performance on already learned tasks. By building upon ideas from network quantization and pruning, we learn binary masks that “piggyback” on an existing network, or are applied to unmodified weights of that network to provide ...More

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