1 Introduction
Variational AutoEncoder (VAE) vae and Generative Adversarial Networks (GAN) ganorig show good performance in modelling realworld data such as images well. The key idea of both frameworks is to map a simple distribution (typically Gaussian) of lower dimension to a highdimensional observation space by a complex nonlinear function (typically neural network). Most of research efforts are concentrated on the enhancement of training procedure and neural architectures giving rise to a variety of elegant extensions for VAE and GANs ganoverview .
We consider prior distribution that is mapped to data distribution as one of design choices when building generative model. Its importance is highlighted in a number of works elbosurgery ; svae18 ; homeovae ; spherenlp . Although svae18 provides an extensive overview of usage of normalized latent variables (points lying on a hypersphere); this is clearly just one of the possible design choices for prior distribution in generative model.
Recent works svae18 ; homeovae ; spherenlp
argued that manifold hypothesis for data
belkinprovides evidence in favor of using more complicated priors than Gaussian, for which the topology of latent space matches that of the data. The above mentioned works derived analytic formulas for reparameterization of probability density on manifold (hypersphere in
svae18 and Lie group in homeovae ).A somewhat less rigorous argument in favor of using manifoldvalued latent variables is that we can represent generative process for data as having two sources of variation (see Figure 1
): one is uniform sampling from a group of transformations that we consider as compact symmetry groups (for example group of rotations) and another one is all the rest. This favors the choice of such topology of the latent space that would match "real" generative process: choose uniform distribution on some compact symmetry group as a prior distribution for latent variables.
Once a universal procedure for fast prototyping of VAE with different manifoldvalued variables is available, such VAE can be used for estimating the likelihood integral
(for example using IWAE estimate iwae ) and thus make conclusions about latent symmetries that are present in the data. This was one of the key motivations for the current work.All of above brings to the focus the case of continuously differentiable symmetry groups (Lie groups), which is a special case of manifoldvalued latent variables.
2 Manifoldvalued latent variables
Let us make the following preliminary assumption:
Data are generated as on Figure 1 with Lie group embedded in and there is a continuous mapping .
When using images as a test bed it implies that images generated by "close" symmetry elements (say two similar rotation angles and ) are also close in the pixel space. It justifies using additional tricks such as continuity loss homeovae for training VAE with manifoldvalued latent variables.
2.1 Construction of VAE
Recall the optimization problem for VAE vae :
where denotes the data distribution, is a posterior distribution on latent space , is the corresponding prior, and is the likelihood of a data point given . In order to construct a VAE with manifoldvalued latent variables, we need the following:

An encoder that produces the posterior distribution from a parametric family of distributions on a manifold.

An ability to sample from this posterior distribution: .

An ability to compute KLdivergence between this posterior and a given prior.
Recent works svae18 ; homeovae proposed approaches to working with manifoldvalued latent variables that are similar in spirit to ours: they derive a reparameterization of probability density defined on smooth manifold and use it in VAE. Problem is that such derivation appears to be complicated and needs to be done for all manifolds of interest.
Our approach is the following. First of all, we introduce a hidden latent space , such that , where is our manifold lying in a latent space of dimension . Let be a prior distribution on .
Suppose then, we have an embedding , so that . Being an embedding requires to be a diffeomorphism with its image, in particular, should be a differentiable injective map. We also pose an additional constraint on : it should map the prior on to a prior on the manifold ; in other words, if , then .
Using this embedding , we can construct a VAE with manifoldvalued latent variables as depicted on the right part of Figure 2. In this case the posterior distribution on together with the embedding induce a posterior distribution on . We then have to compute KLdivergence between this induced posterior and the prior on the manifold. Despite the fact that in this case the probability mass is concentrated on the manifold and hence the probability density on is degenerate, we can define the manifold probability densities and (see Appendix 5.1 for details). Moreover, the corresponding KLdivergence is equivalent to the KLdivergence between distributions defined on (Appendix 4.3):
Hence the final optimization problem for model on the right part of Figure 2 becomes the following:
where are parameters of VAE encoder, which encodes the object into space, and are parameters of VAE decoder which maps the manifold to datamanifold in feature space; is our data distribution.
Thereby working with probability distributions induced on manifold of interest is easy: both terms in VAE loss (reconstruction error and KLdivergence) are easily calculated in the original hidden space
that is further mapped on a manifold.2.2 Learning manifold embedding
To apply the procedure described above, we have to construct an embedding . In order to do this, we propose the following procedure:

Sample data from (distribution on ).

Use the decoder of this trained WAE as our embedding function .
Our motivation is the following: since the dimension of latent space and the dimension of manifold are the same, the reconstruction term in WAE objective constraints its decoder to be an injective map. Since it is represented with a neural network, it is also differentiable. The objective of WAE learning also forces its decoder to map a prior distribution on a latent space (in our case, ) to a distribution of data to the feature space (in our case, ). Hence WAE decoder is an ideal candidate for an embedding .
At first glance the described model leaves quite similar questions as vanilla VAE: we "shifted" the complex task of learning nonhomeomorphic manifolds of a different topology (latent space and data space) from the VAE decoder to submodule of the same VAE but pretrained using WAE. Nevertheless, the procedure ensures better control over mapping to manifold and one can develop corresponding metrics to control the quality of mapping.
3 Introducing symmetries of latent manifold into encoder
Recall that in our scheme an encoder together with embedding induce a family of posterior distributions on ; let us call this family .
A natural requirement to is to have the same symmetries as has. Suppose we have a symmetry group of acting on , i.e.
For example, if is an dimensional sphere in , is a group of rotations . We require to also be a symmetry of also:
This means that if a symmetry of acts on samples from a distribution , we should get samples from another distribution from the same family . Note that we did not pose this requirement while training , hence it would not generally be satisfied. Therefore we have to symmetrize explicitly.
In order to do this we introduce a group action encoder , see Figure 3. This group action encoder produces an element of the symmetry group of , which further acts on a sample . This effectively enriches the posterior family with .
4 Experiments and conclusions
Models  ELBO 

VAE,  
Manifoldlatent VAE with learned ,  
Manifoldlatent VAE with ,  
Manifoldlatent VAE with learned and group action encoder,  
VAE, 
We followed the same experimental setup as for a toy task in paper svae18 , but without noise. ^{1}^{1}1Our code is available on GitHub: https://github.com/varenick/manifold_latent_vae Sampling of a batch from the dataset consisted of two steps:

We generated uniformly distributed points on a 1dimensional unit sphere embedded in .

We applied a nonlinear fixed transformation
implemented as a randomly initialized multilayer perceptron with one hidden layer of size 100 and ReLU nonlinearity. Xavieruniform initialization scheme was applied to the hidden layer.
All models are VAEs with the posterior distribution (Beta on ), the prior distribution (uniform of ) and the likelihood (Gaussian on ). As for the reparameterization function , it was either WAEMMD or the exact mapping from segment into a 1dimensional circle ("Projection") in the first layer of decoder:
The dimensions of latent variables were either 1 or 2.
In a case when the group action encoder is used, it produces an angle (element of ), which is further used to rotate the sample .
The results are presented in Table 1. All decoder structures that include manifold mapping show better results than a vanilla VAE with 1dimensional latent Gaussian space.
Acknowledgments
This work was supported by National Technology Initiative and PAO Sberbank project ID 0000000007417F630002.
References
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5 Appendix
5.1 Probability density functions with manifold support
Suppose we have a probability distribution on with density and a diffeomorphism , where as well. Then, induces a probability distribution on with the following density:
Suppose now that with , and is a smooth embedding (which requires to be a diffeomorphism between and ). From this follows that induces degenerate probability distribution on since all the probability mass in is concentrated on a manifold . The corresponding probability measure is trivial:
for some event on . Although we cannot define a valid probability density of , we can define a manifold probability density on as follows:
where by we denote an dimensional volume of ; let us define this volume. Let be an open subset of . Then its image under embedding is an open subset of a manifold (open in terms of the topology of ). If is a Euclidean space, than the "volume" of is given simply as:
Since is embedded into , and is a Euclidean space, we can measure an dimensional "volume" of . It is given as:
where
is a metric tensor on
, induced by the scalar product on and the embedding :Returning to our formula for probability density on , we now have:
Or,
5.2 Calculation of KL divergence in the case of normalizing flow
where is the posterior distribution (i.e. fullyfactorized Gauss or Beta) on latent variables of WAE, which we use for manifold embedding, is the corresponding prior (i.e. standard Gauss or Uniform), is the decoder of the WAE, which we use to transform the latent space of WAE into manifold , and is the Jacobian of this transformation. As we see, logdeterminants of Jacobians cancel out, and we are left with the KLdivergence on latent space of WAE.
5.3 Calculation of KL divergence in case of embedding map
where denotes the metric tensor of the embedding . As in Appendix 4.2, the corresponding terms cancel out.
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