Guided Variational Autoencoder for Disentanglement Learning
By Zheng Ding, Yifan Xu, Weijian Xu, Gaurav Parmar, Yang Yang, Max Welling, Zhuowen Tu. 2020
126 citations recorded by the source corpus.
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 signal 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 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.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Generative Models, Representation & Self-Supervised Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computer Vision and Pattern Recognition
- Wangmeng Zuo
- Luc Van Gool
- Changyou Chen
- Ming-Hsuan Yang
- Kwonjoon Lee
- Zhuowen Tu
- Lei Zhang
- Dacheng Tao
In Generative Models
In Representation & Self-Supervised Learning
Citations in the corpus
- Dual Contradistinctive Generative Autoencoder (2020, positive): “Results derived from [12].”
- Unpaired Image-to-Image Translation via Latent Energy Transport (2020, background): “β-VAE [14, 15], Factor-VAE [20], GuidedVAE [8], TCVAE [20] and Adversarial Latent AE [32].”
- Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation (2021, methodology): “Guided-VAE [9] used an additional discriminator to guide the unsupervised disentanglement learning and learned the latent geometric transformation and principal components.”