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MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglement
[article]
2020
arXiv
pre-print
Preserved Fulltext
We present MRGAN, a multi-rooted adversarial network which generates part-disentangled 3D point-cloud shapes without part-based shape supervision. ...
a set of loss terms designed with part disentanglement and shape semantics in mind. ...
Model
Conclusions We presented MRGAN, a multi-rooted architecture that addresses the challenging task of learning a part-disentangled representation in an unsupervised manner. ...
arXiv:2007.12944v1
fatcat:7twrpqwk5feftkughbzykwui4m
A Survey on Generative Adversarial Networks: Variants, Applications, and Training
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The Generative Models have gained considerable attention in the field of unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to its outstanding data ...
Many models of GAN have proposed, and several practical applications emerged in various domains of computer vision and machine learning. ...
Recently, the Disentangled Representation Net (DRNET) [170] approach learns disentangled image representations from the video. ...
arXiv:2006.05132v1
fatcat:gyjezuh5sfdilkp43ydsea5cwa