Paper Info
Title
KSM: Fast Multiple Task Adaption via Kernel-wise Soft Mask Learning
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.
Year
DOI
Venue
2021
10.1109/CVPR46437.2021.01363
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
DocType
ISSN
Citations 
Conference
1063-6919
0
PageRank 
References 
Authors
0.34
8
4
Name
Order
Citations
PageRank
Li Yang1104.50
Zhezhi He213625.37
Junshan Zhang32905220.99
Deliang Fan437553.66