Vote for your favorite mineral in #MinCup25! - Chrysotile vs. Pectolite
Two new minerals to launch this year's cup! Hazardous (but useful!) chrysotile faces off against stunning gem pectolite.
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Bhatt, Gaurav, Jha, Piyush, Raman, Balasubramanian (2019) Representation learning using step-based deep multi-modal autoencoders. Pattern Recognition, 95. 12-23 doi:10.1016/j.patcog.2019.05.032

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleRepresentation learning using step-based deep multi-modal autoencoders
JournalPattern Recognition
AuthorsBhatt, GauravAuthor
Jha, PiyushAuthor
Raman, BalasubramanianAuthor
Year2019 (November)Volume95
PublisherElsevier BV
DOIdoi:10.1016/j.patcog.2019.05.032Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID9367886Long-form Identifiermindat:1:5:9367886:1
GUID0
Full ReferenceBhatt, Gaurav, Jha, Piyush, Raman, Balasubramanian (2019) Representation learning using step-based deep multi-modal autoencoders. Pattern Recognition, 95. 12-23 doi:10.1016/j.patcog.2019.05.032
Plain TextBhatt, Gaurav, Jha, Piyush, Raman, Balasubramanian (2019) Representation learning using step-based deep multi-modal autoencoders. Pattern Recognition, 95. 12-23 doi:10.1016/j.patcog.2019.05.032
In(2019) Pattern Recognition Vol. 95. Elsevier BV

See Also

These are possibly similar items as determined by title/reference text matching only.

Zhou, Tongxue (2024) Multi-modal brain tumor segmentation via disentangled representation learning and region-aware contrastive learning. Pattern Recognition, 149. 110282 doi:10.1016/j.patcog.2024.110282
Yang, Weili, Huang, Junduan, Luo, Dacan, Kang, Wenxiong (2024) Efficient disentangled representation learning for multi-modal finger biometrics. Pattern Recognition, 145. 109944 doi:10.1016/j.patcog.2023.109944
Xing, Hong-Jie, Wu, Hui-Nan, Zhang, Ping-Ping (2024) Deep multi-sphere support vector data description based on disentangled representation learning. Pattern Recognition, 156. 110842 doi:10.1016/j.patcog.2024.110842
Lin, Chenhao, Kumar, Ajay (2018) Contactless and partial 3D fingerprint recognition using multi-view deep representation. Pattern Recognition, 83. 314-327 doi:10.1016/j.patcog.2018.05.004
Zhang, Ming, Khan, Sheheryar, Yan, Hong (2020) Deep eigen-filters for face recognition: Feature representation via unsupervised multi-structure filter learning. Pattern Recognition, 100. 107176pp. doi:10.1016/j.patcog.2019.107176
Xu, Xiangyang, Li, Yuncheng, Wu, Gangshan, Luo, Jiebo (2017) Multi-modal deep feature learning for RGB-D object detection. Pattern Recognition, 72. 300-313 doi:10.1016/j.patcog.2017.07.026
Hu, Minghui, Suganthan, P.N. (2022) Representation learning using deep random vector functional link networks for clustering. Pattern Recognition, 129. 108744 doi:10.1016/j.patcog.2022.108744
Ren, Liangliang, Lu, Jiwen, Feng, Jianjiang, Zhou, Jie (2017) Multi-modal uniform deep learning for RGB-D person re-identification. Pattern Recognition, 72. 446-457 doi:10.1016/j.patcog.2017.06.037
Sellami, Akrem, Tabbone, Salvatore (2022) Deep neural networks-based relevant latent representation learning for hyperspectral image classification. Pattern Recognition, 121. 108224pp. doi:10.1016/j.patcog.2021.108224
Zhang, Duoyi, Bashar, Md Abul, Nayak, Richi (2025) A novel multi-modal fusion method based on uncertainty-guided meta-learning. Pattern Recognition, 158. doi:10.1016/j.patcog.2024.110993
Zhu, Yi, Wu, Xindong, Qiang, Jipeng, Hu, Xuegang, Zhang, Yuhong, Li, Peipei (2022) Representation learning with deep sparse auto-encoder for multi-task learning. Pattern Recognition, 129. 108742 doi:10.1016/j.patcog.2022.108742
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are Β© OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: September 1, 2025 01:29:23
Go to top of page