硕士生导师
个人信息Personal Information
学历：博士研究生毕业
学位：工学博士学位
办公地点：犀浦3号教学楼31529
毕业院校：四川大学
学科：电子信息. 软件工程. 计算机应用技术
所在单位：计算机与人工智能学院
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Self-supervised Discriminative Representation Learning by Fuzzy Autoencoder
影响因子：10.489
DOI码：10.1145/1122445.1122456
所属单位：西南交通大学
发表刊物：ACM Transactions on Intelligent Systems and Technology
关键字：Additional Key Words and Phrases: autoencoders, discriminative representation learning, fuzzy clustering, self-supervised learning
摘要：Representation learning based on autoencoders has received great concern for its potential ability to capture valuable latent information. Conventional autoencoders (AE) pursue minimal reconstruction error, but in most machine learning tasks such as classification and clustering, the discrimination of feature representation is also important. To address this limitation, an enhanced self-supervised discriminative fuzzy autoencoder (FAE) is innovatively proposed, which focuses on exploring information within data to guide the unsupervised training process and enhancing feature discrimination in a self-supervised manner. In FAE, fuzzy membership is applied to provide a means of self-supervised, which allows FAE can not only utilize AE’s outstanding representation learning capabilities but can also transform the original data into another space with improved discrimination. Firstly, the objective function corresponding to FAE is proposed by reconstruction loss and clustering oriented loss simultaneously. Subsequently, Mini-Batch Gradient Descent (MBGD) is applied to infer the objective function and the detailed process is illustrated step by step. Finally, empirical studies on clustering tasks have demonstrated the superiority of FAE over the state-of-the-art
合写作者：Yinghui Zhang, Zehao Liu,李天瑞
第一作者：Wenlu Yang
论文类型：学术论文
通讯作者：Hongjun Wang
学科门类：工学
一级学科：计算机科学与技术
卷号：Vol. 37
期号：No. 4, Article 111
页面范围：19 pages
ISSN号：2157-6904
是否译文：否