KSM: Fast Multiple Task Adaption via Kernel-wise Soft Mask Learning

Li Yang, Zhezhi He, Junshan Zhang, Deliang Fan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Deep Neural Networks (DNN) could forget the knowledge about earlier tasks when learning new tasks, which is known as catastrophic forgetting. To learn new task without forgetting, recently, the mask-based learning method (e.g. piggyback [10]) is proposed to address this issue by learning only a binary element-wise mask, while keeping the backbone model fixed. However, the binary mask has limited modeling capacity for new tasks. A more recent work [5] proposes a compress-grow-based method (CPG) to achieve better accuracy for new tasks by partially training backbone model, but with order-higher training cost, which makes it infeasible to be deployed into popular state-of-the-art edge-/mobile-learning. The primary goal of this work is to simultaneously achieve fast and high-accuracy multi task adaption in continual learning setting. Thus motivated, we propose a new training method called Kernel-wise Soft Mask (KSM), which learns a kernel-wise hybrid binary and real-value soft mask for each task. Such a hybrid mask can be viewed as a superposition of a binary mask and a properly scaled real-value tensor, which offers a richer representation capability without low-level kernel support to meet the objective of low hardware overhead. We validate KSM on multiple benchmark datasets against recent state-of-the-art methods (e.g. Piggyback, Packnet, CPG, etc.), which shows good improvement in both accuracy and training cost.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
PublisherIEEE Computer Society
Pages13840-13848
Number of pages9
ISBN (Electronic)9781665445092
DOIs
StatePublished - 2021
Event2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021 - Virtual, Online, United States
Duration: Jun 19 2021Jun 25 2021

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Conference

Conference2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
Country/TerritoryUnited States
CityVirtual, Online
Period6/19/216/25/21

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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