Knowledge Commons of Institute of Automation,CAS
Multimodal deep generative adversarial models for scalable doubly semi-supervised learning | |
Du, Changde1,2,3,4; Du, Changying5; He, Huiguang1,2,3,6 | |
发表期刊 | INFORMATION FUSION |
ISSN | 1566-2535 |
2021-04-01 | |
卷号 | 68页码:118-130 |
摘要 | The comprehensive utilization of incomplete multi-modality data is a difficult problem with strong practical value. Most of the previous multimodal learning algorithms require massive training data with complete modalities and annotated labels, which greatly limits their practicality. Although some existing algorithms can be used to complete the data imputation task, they still have two disadvantages: (1) they cannot control the semantics of the imputed modalities accurately; and (2) they need to establish multiple independent converters between any two modalities when extended to multimodal cases. To overcome these limitations, we propose a novel doubly semi-supervised multimodal learning (DSML) framework. Specifically, DSML uses a modality-shared latent space and multiple modality-specific generators to associate multiple modalities together. Here we divided the shared latent space into two independent parts, the semantic labels and the semantic-free styles, which allows us to easily control the semantics of generated samples. In addition, each modality has its own separate encoder and classifier to infer the corresponding semantic and semantic-free latent variables. The above DSML framework can be adversarially trained by using our specially designed softmax-based discriminators. Large amounts of experimental results show that the DSML obtains better performance than the baselines on three tasks, including semi-supervised classification, missing modality imputation and cross-modality retrieval. |
关键词 | Multiview learning Multimodal fusion Generative adversarial networks Deep generative models Semi-supervised learning |
DOI | 10.1016/j.inffus.2020.11.003 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61976209] ; National Natural Science Foundation of China[62020106015] ; National Natural Science Foundation of China[61906188] ; Chinese Academy of Sciences (CAS) International Collaboration Key, China[173211KYSB20190024] ; Strategic Priority Research Program of CAS, China[XDB32040000] |
项目资助者 | National Natural Science Foundation of China ; Chinese Academy of Sciences (CAS) International Collaboration Key, China ; Strategic Priority Research Program of CAS, China |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods |
WOS记录号 | WOS:000616409600009 |
出版者 | ELSEVIER |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 机器学习 |
国重实验室规划方向分类 | 多模态协同感认知智能的机制机理与数学建模 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/43266 |
专题 | 脑图谱与类脑智能实验室_神经计算与脑机交互 |
通讯作者 | He, Huiguang |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 4.Huawei Cloud BU EI Innovat Lab, Beijing 100085, Peoples R China 5.Huawei Noahs Ark Lab, Beijing 100085, Peoples R China 6.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China |
第一作者单位 | 中国科学院自动化研究所; 模式识别国家重点实验室 |
通讯作者单位 | 中国科学院自动化研究所; 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Du, Changde,Du, Changying,He, Huiguang. Multimodal deep generative adversarial models for scalable doubly semi-supervised learning[J]. INFORMATION FUSION,2021,68:118-130. |
APA | Du, Changde,Du, Changying,&He, Huiguang.(2021).Multimodal deep generative adversarial models for scalable doubly semi-supervised learning.INFORMATION FUSION,68,118-130. |
MLA | Du, Changde,et al."Multimodal deep generative adversarial models for scalable doubly semi-supervised learning".INFORMATION FUSION 68(2021):118-130. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Information Fusion.p(2917KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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