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169,051 papers · 148 categories

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0111 · Apr 201819922001200920182026
2 results for back-distillation

New method transplants specific neural networks to generic ones without training samples.

problem Learning massive tasks and categories requires collecting samples for all at once.
method Designs a functionally interpretable generic network and uses back-distillation for transplanting.
result Method without training samples outperforms with 100 samples.

This paper focuses on a new task, i.e., transplanting a category-and-task-specific neural network to a generic, modular network without strong supervision. We design an functionally interpretable structure for the generic network. Like building LEGO blocks, we teach the generic network a new category by directly transp…

2018-04-26abs ↗pdf ↗