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Title: Guided variational autoencoder for disentanglement learning

Paper content:
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.
The resurgence of autoencoders (AE) (LeCun, 1987; Bourlard & Kamp, 1988; Hinton & Zemel, 1994) is an important component in the rapid development of modern deep learning .
Autoencoders have been widely adopted for modeling signals and images (Poultney et al., 2007; Vincent et al., 2010) .
Its statistical counterpart, the variational autoencoder (VAE) (Kingma & Welling, 2014) , has led to a recent wave of development in generative modeling due to its two-in-one capability, both representation and statistical learning in a single framework.
Another exploding direction in generative modeling includes generative adversarial networks (GAN) Goodfellow et al. (2014) , but GANs focus on the generation process and are not aimed at representation learning (without an encoder at least in its vanilla version).
Compared with classical dimensionality reduction methods like principal component analysis (PCA) (Hotelling, 1933; Jolliffe, 2011) and Laplacian eigenmaps (Belkin & Niyogi, 2003) , VAEs have demonstrated their unprecedented power in modeling high dimensional data of real-world complexity.
However, there is still a large room to improve for VAEs to achieve a high quality reconstruction/synthesis.
Additionally, it is desirable to make the VAE representation learning more transparent, interpretable, and controllable.
In this paper, we attempt to learn a transparent representation by introducing guidance to the latent variables in a VAE.
We design two strategies for our Guided-VAE, an unsupervised version ( Fig. 1 .a) and a supervised version ( Fig. 1.b) .
The main motivation behind Guided-VAE is to encourage the latent representation to be semantically interpretable, while maintaining the integrity of the basic VAE architecture.
Guided-VAE is learned in a multi-task learning fashion.
The objective is achieved by taking advantage of the modeling flexibility and the large solution space of the VAE under a lightweight target.
Thus the two tasks, learning a good VAE and making the latent variables controllable, become companions rather than conflicts.
In unsupervised Guided-VAE, in addition to the standard VAE backbone, we also explicitly force the latent variables to go through a lightweight encoder that learns a deformable PCA.
As seen in Fig. 1 .a
, two decoders exist, both trying to reconstruct the input data x: Dec main .
The main decoder, denoted as Dec main , functions regularly as in the standard VAE (Kingma & Welling, 2014) ; the secondary decoder, denoted as Dec sub , explicitly learns a geometric deformation together with a linear sub-space.
In supervised Guided-VAE, we introduce a subtask for the VAE by forcing one latent variable to be discriminative (minimizing the classification error) while making the rest of the latent variable to be adversarially discriminative (maximizing the minimal classification error).
This subtask is achieved using an adversarial excitation and inhibition formulation.
Similar to the unsupervised Guided-VAE, the training process is carried out in an end-to-end multi-task learning manner.
The result is a regular generative model that keeps the original VAE properties intact, while having the specified latent variable semantically meaningful and capable of controlling/synthesizing a specific attribute.
We apply Guided-VAE to the data modeling and few-shot learning problems and show favorable results on the MNIST, CelebA, and Omniglot datasets.
The contributions of our work can be summarized as follows:
• We propose a new generative model disentanglement learning method by introducing latent variable guidance to variational autoencoders (VAE).
Both unsupervised and supervised versions of Guided-VAE have been developed.
• In unsupervised Guided-VAE, we introduce deformable PCA as a subtask to guide the general VAE learning process, making the latent variables interpretable and controllable.
• In supervised Guided-VAE, we use an adversarial excitation and inhibition mechanism to encourage the disentanglement, informativeness, and controllability of the latent variables.
Guided-VAE is able to keep the attractive properties of the VAE and it is easy to implement.
It can be trained in an end-to-end fashion.
It significantly improves the controllability of the vanilla VAE and is applicable to a range of problems for generative modeling and representation learning.
In this paper we have presented a new representation learning method, guided variational autoencoder (Guided-VAE), for disentanglement learning.
Both versions of Guided-VAE utilize lightweight guidance to the latent variables to achieve better controllability and transparency.
Improvements on disentanglement, image traversal, and meta-learning over the competing methods are observed.
Guided-VAE maintains the backbone of VAE and can be applied to other generative modeling applications.

assistant: text: Learning a controllable generative model by performing latent representation disentanglement learning.