Semi‐Supervised 3D Shape Recognition via Multimodal Deep Co‐training

Author:

Song Mofei12ORCID,Liu Yu1ORCID,Liu Xiao Fan3ORCID

Affiliation:

1. School of Computer Science and Engineering Southeast University P.R. China

2. State Key Laboratory for Novel Software Technology Nanjing University P. R. China

3. Web Mining Laboratory, Department of Media and Communication City University of Hong Kong Hong Kong SAR China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Computer Graphics and Computer-Aided Design

Reference52 articles.

1. AchlioptasP. DiamantiO. MitliagkasI. GuibasL.: Learning representations and generative models for 3D point clouds. InProceedings of the 35th International Conference on Machine Learning(Stockholmsmässan Stockholm Sweden 10–15 Jul2018) Dy J. Krause A. (Eds.) vol. 80 ofProceedings of Machine Learning Research PMLR pp.40–49. 2

2. AhmedE. SaintA. ShabayekA. E. R. CherenkovaK. DasR. GusevG. AouadaD. OtterstenB.: A survey on deep learning advances on different 3d data representations.arXiv: Computer Vision and Pattern Recognition(2018). 1 2

3. BalcanM.‐F. BlumA. YangK.: Co‐training and expansion: Towards bridging theory and practice. InNIPS(2004). 1

4. Learning High-Level Feature by Deep Belief Networks for 3-D Model Retrieval and Recognition

5. Combining labeled and unlabeled data with co‐training;Blum A.;COLT',1998

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