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.

设计一种算法 Guided-VAE，通过提供信号来实现 VAE 的潜在编码 / 嵌入，以学习可控的生成模型，该算法在表示学习，合成 / 采样改进，分类中取得了更好的效果