双分支迭代的深度增量图像分类方法

doi:10.16451/j.cnki.issn1003-6059.202002007

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Abstract To solve the catastrophic forgetting problem caused by incremental learning, a deep incremental image classification method based on double-branch iteration is proposed. The primary network is utilized to store the acquired old class knowledge, while the branch network is exploited to learn the new class knowledge. The parameters of the branch network are optimized by the weight of the primary network in the incremental iteration process. Density peak clustering method is employed to select typical samples from the iterative dataset and construct retention set. The retention set is added into the incremental iteration training to mitigate catastrophic forgetting. The experiments demonstrate the better performance of the proposed method.

Key words： Double Branch Convolutional Neural Network Retention Set Incremental Learning Deep Learning Image Classification

Received: 23 October 2019

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.1170011574,61502331), Natural Science Foundation of Tianjin(No.16JCYBJC42000,18JCYBJC85100), Tianjin Enterprise Technology Commissioner Project(No.19JCTPJC56300), Scien- tific Research Plan Project of Tianjin Municipal Education Commission(No.2017KJ237)

Corresponding Authors: HE Li, Ph.D., professor. Her research interests include data mining and machine learning.

About author:: HAN Keping, master student. Her research interests include machine learning and incremental learning; ZHU Hongxi, master student. Her research interests include machine learning and image processing; LIU Ying, master student. Her research interests include data mining and machine learning.

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Cite this article:

HE Li,HAN Keping,ZHU Hongxi等. Deep Incremental Image Classification Method Based on Double-Branch Iteration[J]. , 2020, 33(2): 150-159.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202002007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I2/150

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