Title
DEEP ENSEMBLE SIAMESE NETWORK FOR INCREMENTAL SIGNAL CLASSIFICATION
Abstract
Incremental Signal Classification (ISC) aims to continuously identify and classify unknown signal categories, which is essentially an open-set classification task. In this paper, a new Deep Ensemble Siamese Network (DESN) is constructed for unknown category detection and incremental accumulation of signals from the detected category. Then the accumulated samples are used to update a One-dimensional Convolution Network (OCN) for incremental learning of new signal categories. Experimental results show that the proposed method can achieve accurate detection and accumulation of unknown signals, and is feasible for practical ISC.
Year
DOI
Venue
2021
10.1109/ICASSP39728.2021.9414902
2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021)
Keywords
DocType
Citations 
signal recognition, incremental learning, Siamese network, novelty detection
Conference
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Chen Yang1102.96
Shuyuan Yang2537.60