Manifold Adversarial Learning for Cross-domain 3D Shape Representation-Reference-Cited by-同舟云学术

Manifold Adversarial Learning for Cross-domain 3D Shape Representation

Published:2022 Issue: Volume: Page:272-289
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Huang Hao,Chen Cheng,Fang Yi

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-19809-0_16

Reference47 articles.

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3. Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Vaughan, J.W.: A theory of learning from different domains. Mach. Learn. 79(1), 151–175 (2010)

4. Blanchard, G., Lee, G., Scott, C.: Generalizing from several related classification tasks to a new unlabeled sample. In: Advances in Neural Information Processing Systems, vol. 24 (2011)

5. Boulch, A.: Convpoint: Continuous convolutions for point cloud processing. Comput. Graph. 88, 24–34 (2020)

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