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Title: Guided Variational Autoencoder for Disentanglement Learning
We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning. The learning objective is achieved by providing signals to the latent encoding/embedding in VAE without changing its main backbone architecture, hence retaining the desirable properties of the VAE. We design an unsupervised strategy and a supervised strategy in Guided-VAE and observe enhanced modeling and controlling capability over the vanilla VAE. In the unsupervised strategy, we guide the VAE learning by introducing a lightweight decoder that learns latent geometric transformation and principal components; in the supervised strategy, we use an adversarial excitation and inhibition mechanism to encourage the disentanglement of the latent variables. Guided-VAE enjoys its transparency and simplicity for the general representation learning task, as well as disentanglement learning. On a number of experiments for representation learning, improved synthesis/sampling, better disentanglement for classification, and reduced classification errors in meta learning have been observed.  more » « less
Award ID(s):
1618477 1717431
PAR ID:
10166844
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN:
2332-564X
Page Range / eLocation ID:
7920-7929
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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