基于潜层向量对齐的持续零样本学习算法

doi:10.16451/j.cnki.issn1003-6059.202112008

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Abstract Continual zero-shot learning aims to accumulate the knowledge of seen classes and utilize the knowledge for unseen classes recognition. However, catastrophic forgetting can easily occur in continual learning. Therefore, a continual zero-shot learning algorithm based on latent vectors alignment is proposed. Based on the cross and distribution aligned variational auto-encoder network, the visual latent vectors of current tasks and learned tasks are aligned to enhance the similarity of latent space of different tasks. Selective retraining is adopted to improve the discrimination ability of the current task model for learned tasks. For different tasks, the independent classifiers are trained with visual-hidden vectors of the seen classes and semantic-hidden vectors of the unseen classes to achieve zero-shot image classification. Extensive experiments on four standard datasets show that the proposed algorithm completes the continual zero-shot recognition task effectively and alleviates the catastrophic forgetting.

Key words： Continual Zero-Shot Learning Catastrophic Forgetting Latent Vectors Alignment Selective Retraining

Received: 23 June 2021

ZTFLH:

TP 391.4

Fund:National Natural Science Foundation of China(No.62076075)

Corresponding Authors: HU Xiao, Ph.D., professor. His research interests include machine learning, machine vision and video behavior analysis.

About author:: ZHONG Xiaorong, master student. Her research interests include computer vision and zero shot learning.
DING Jiayu, master student. His research interests include artificial intelligence, computer vision and zero shot learning.

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	ZHONG Xiaorong
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	DING Jiayu

Cite this article:

ZHONG Xiaorong,HU Xiao,DING Jiayu. Continual Zero-Shot Learning Algorithm Based on Latent Vectors Alignment[J]. , 2021, 34(12): 1152-1159.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202112008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I12/1152

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