Guided Variational Autoencoder for Disentanglement Learning
This work addresses the challenge of disentanglement learning for representation learning tasks, offering incremental improvements in generative modeling and classification.
The authors tackled the problem of learning controllable generative models through disentangled latent representations, proposing Guided-VAE which enhances modeling and control capabilities over vanilla VAE, leading to improved synthesis, better disentanglement for classification, and reduced classification errors in meta-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.