Review on autoencoder and its application
As a typical deep unsupervised learning model, autoencoder can automatically learn effective abstract features from unlabeled samples.In recent years, autoencoder has been widely used in target recognition, intrusion detection, fault diagnosis and many other fields.Thus, the theoretical basis, impro...
| Үндсэн зохиолчид: | , , , , |
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| Формат: | Өгүүллэг |
| Хэл сонгох: | zho |
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Editorial Department of Journal on Communications
2021-09-01
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| Цуврал: | Tongxin xuebao |
| Нөхцлүүд: | |
| Онлайн хандалт: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021160/ |
| _version_ | 1826874791726940160 |
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| author | Jie LAI Xiaodan WANG Qian XIANG Yafei SONG Wen QUAN |
| author_facet | Jie LAI Xiaodan WANG Qian XIANG Yafei SONG Wen QUAN |
| author_sort | Jie LAI |
| collection | DOAJ |
| description | As a typical deep unsupervised learning model, autoencoder can automatically learn effective abstract features from unlabeled samples.In recent years, autoencoder has been widely used in target recognition, intrusion detection, fault diagnosis and many other fields.Thus, the theoretical basis, improved methods, application fields and research directions of autoencoder were described and summarized comprehensively.At first, the network structure, theoretical derivation and algorithm flow of traditional autoencoder were introduced and analyzed, and the difference between autoencoder and other unsupervised learning algorithms was compared.Then, common improved autoencoders were discussed, and their innovation, improvement methods and relative merits were analyzed.Next, the practical application status of autoencoder in target recognition, intrusion detection and other fields were introduced.At last, the existing problems of autoencoder were summarized, and the possible research directions were prospected. |
| first_indexed | 2025-02-17T00:40:02Z |
| format | Article |
| id | doaj.art-cd01fda62b5441bc8d1b51e50f5a33fd |
| institution | Directory Open Access Journal |
| issn | 1000-436X |
| language | zho |
| last_indexed | 2025-02-17T00:40:02Z |
| publishDate | 2021-09-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj.art-cd01fda62b5441bc8d1b51e50f5a33fd2025-01-14T07:22:49ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2021-09-014221823059744932Review on autoencoder and its applicationJie LAIXiaodan WANGQian XIANGYafei SONGWen QUANAs a typical deep unsupervised learning model, autoencoder can automatically learn effective abstract features from unlabeled samples.In recent years, autoencoder has been widely used in target recognition, intrusion detection, fault diagnosis and many other fields.Thus, the theoretical basis, improved methods, application fields and research directions of autoencoder were described and summarized comprehensively.At first, the network structure, theoretical derivation and algorithm flow of traditional autoencoder were introduced and analyzed, and the difference between autoencoder and other unsupervised learning algorithms was compared.Then, common improved autoencoders were discussed, and their innovation, improvement methods and relative merits were analyzed.Next, the practical application status of autoencoder in target recognition, intrusion detection and other fields were introduced.At last, the existing problems of autoencoder were summarized, and the possible research directions were prospected.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021160/autoencoderdeep learningunsupervised learningfeature extractionregularization |
| spellingShingle | Jie LAI Xiaodan WANG Qian XIANG Yafei SONG Wen QUAN Review on autoencoder and its application Tongxin xuebao autoencoder deep learning unsupervised learning feature extraction regularization |
| title | Review on autoencoder and its application |
| title_full | Review on autoencoder and its application |
| title_fullStr | Review on autoencoder and its application |
| title_full_unstemmed | Review on autoencoder and its application |
| title_short | Review on autoencoder and its application |
| title_sort | review on autoencoder and its application |
| topic | autoencoder deep learning unsupervised learning feature extraction regularization |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021160/ |
| work_keys_str_mv | AT jielai reviewonautoencoderanditsapplication AT xiaodanwang reviewonautoencoderanditsapplication AT qianxiang reviewonautoencoderanditsapplication AT yafeisong reviewonautoencoderanditsapplication AT wenquan reviewonautoencoderanditsapplication |