Structured Disentangling Networks for Learning Deformation Invariant Latent Spaces

Structured Disentangling Networks for Learning Deformation Invariant Latent Spaces

abstract: Disentangling latent spaces is an important research direction in the interpretability of unsupervised machine learning. Several recent works using deep learning are very effective at producing disentangled representations. However, in the unsupervised setting, there is no way to pre-spec...

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Bibliographic Details
Other Authors: Koneripalli, Kaushik (Author)
Format: Dissertation
Language:English
Published: 2019
Subjects:
Electrical engineering
Computer science
Affine transforms
Autoencoders
Disentanglement
Invariance
Latent spaces
Online Access:http://hdl.handle.net/2286/R.I.54893

Internet

http://hdl.handle.net/2286/R.I.54893

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