A Geometric Perspective on Variational Autoencoders
Anonymous Author(s)
AffiliationAddress email
Abstract
This paper introduces a new interpretation of the Variational Autoencoder framework by taking a fully geometric point of view.
We indeed argue that vanilla VAEmodels unveil naturally a Riemannian structure in their latent space and that takinginto consideration those geometrical aspects can lead to better interpolations andan improved generation procedure.
This new proposed sampling method consistsin sampling from the uniform distribution deriving intrinsically from the learned
Riemannian latent space
and we show that using this scheme can make a vanilla
VAE competitive and even better than more advanced versions on several benchmark datasets.
Since generative models are known to be sensitive to the number oftraining samples we also stress the method’s robustness in the low data regime
Introduction
Variational Autoencoders (VAE) [25, 41] are powerful generative models that map complex input datainto a much lower dimensional space referred to as the latent space while driving the latent variablesto follow a given prior distribution.
Their simplicity to use in practice has made them very attractivemodels to perform various tasks such as high-fidelity image generation [39], speech modeling [5],clustering [50] or data augmentation [9].
Nonetheless, when taken in their simplest version, it was noted that these models produce most of thetime blurry samples.
This undesired behavior may be due to several limitations of the VAE framework.
First, the training of a VAE aims at maximizing the Evidence Lower BOund (ELBO) which is only alower bound on the true likelihood and so does not ensure that we are always actually improving thetrue objective [6, 1, 21, 13, 51].
Second, the prior distribution used as regularization and for samplingmay be too simplistic [14] leading to poor data generation and there exists no guarantee that theactual distribution of the latent code will match a given prior distribution inducing over-regularization[12].
Hence, trying to tackle those limitations through richer posterior distributions [44, 40] orbetter priors [48] represents a major part of the proposed improvements over the past few years.
However, the tractability of the ELBO constrains the choice in distributions and so finding a trade-off between model expressiveness and tractability remains crucial.
In this paper, we take a rather different approach and focus on the geometrical aspects a vanilla VAE is able to capture in its latent space.
Inparticular, we propose the following contributions:
• We show that VAEs unveil naturally a latent space with a structure that can be modeled as a
Riemannian manifold through the learned covariance matrices in the variational posterior
distributions and that such modeling can lead to better interpolations.
• We propose a natural sampling scheme consisting in sampling from a uniform distribution
defined on the learned manifold and given by the Riemannian metric.
We show that
this procedure improves significantly the generation process from a vanilla VAE without
complexifying the model nor the training and makes it able to outperform more advanced
VAE models in terms of Frechet Inception Distance [20] and Precision and Recall [43]scores on four benchmark datasets.•
We show that the method appears robust to dataset size changes and outperforms even morestrongly peers when only smaller sample sizes are considered.• We discuss the link of the proposed metric to the pull-back metric.
Variational autoencoders
Considering that we are given x ∈ R D a set of data points deriving from an unknown distributionp ( x ) , a VAE aims at inferring p with a parametric model { p θ , θ ∈ Θ } using a maximum likelihoodestimator.
A key assumption behind the VAE is to assume that the generation process involves latentvariables z living in a lower dimensional space such that the generative model writes
[Equation]
where p is a prior distribution over the latent variables often taken as a standard Gaussian and p θ ( x | z )is referred to as the decoder and is most of the time taken as a parametric distribution the parametersof which are estimated using neural networks.
Hence, the likelihood p θ writes:
[Equation]
As this integral is most of the time intractable so is p θ ( z | x ) , the posterior distribution.
Hence,
Variational Inference [23] is used and a simple parametrized variational distribution q ϕ ( z | x ) isintroduced to approximate the posterior p θ ( z | x ) .
q ϕ ( z | x ) is referred to as the encoder and, in thevanilla VAE, q ϕ is chosen as a multivariate Gaussian whose parameters µ ϕ and Σ ϕ are again givenby neural networks.
An unbiased estimate ˆ p θ of the likelihood p θ ( x ) can then be derived usingimportance sampling with q ϕ ( z | x ) and the ELBO objective follows using Jensen’s inequality:
[Equation]
The ELBO is now tractable since both q ϕ ( z | x ) and p θ ( x | z ) are known and so can be optimized withrespect to the encoder and decoder parameters.
Remark 2.1 .
In practice, p θ ( x | z ) is chosen depending on the modeling of the input data but is oftentaken as a simple distribution ( e.g fixed variance Gaussian, Bernoulli ...) and a weight β can beapplied to balance the weight of the KL term [21].
Hence, the ELBO can also be seen as a two termsobjective [18].
The first one is a reconstruction term given by p θ ( x | z ) while the second one is aregularizer given by the KL between the variational posterior q ϕ and the prior p .
For instance, in thecase of a fixed variance Gaussian for p θ ( x | z ) we have
[Equation]
Related work
A natural way to improve the generation from VAEs consists in trying to use more complex priors[22] than the standard Gaussian distribution used in the initial version such that they better matchthe true distribution of the latent codes.
For instance, using a Mixture of Gaussian [33, 16] or a
Variational Mixture of Posterior (VAMP) [48] as priors was proposed.
In the same vein, hierarchicallatent variable models [46, 26] or prior learning [11, 2] have recently emerged and aimed at findingthe best suited prior distribution for a given dataset.
Acceptance/rejection sampling method was alsoproposed to try to improve the expressiveness of the prior distribution [4].
Some recent works linkingenergy-based models (EBM) and VAEs [49] or modeling the prior as an EBM [36] have demonstratedpromising results and are also worth citing.
On the ground that the latent space must adapt to the data as well, geometry-aware latent spacemodelings as hypershpere [15], torus [17] or Poincaré disk [32] or discrete latent representations[39] were proposed.
Other recent contributions proposed to see the latent space as a Riemannianmanifold where the Riemannian metric is given by the Jacobian of the generator function [3, 10, 45].
This metric was then used directly within the prior modeled by Brownian motions [24].
Others proposed to learn the metric directly from the data throughout training thanks to geometry-awarenormalizing flows [8] or learn the latent structure of the data using transport operators [12].
Whilethese geometry-based methods show interesting properties of the learned latent space they eitherrequire the computation of a time consuming model-dependent function, the Jacobian, or add furtherparameters to the model to learn the metric or transport operators adding some computational burden.
Arguing that VAEs are essentially autoencoders regularized with a Gaussian noise, Ghosh et al.
[18]proposed another interesting interpretation of the VAE framework and showed that other types ofregularization may be of interest as well.
Since the generation process from these autoencoders isno longer relying on the prior distribution, the authors proposed to use ex-post density estimationby fitting simple distributions such as a Gaussian mixture in the latent space.
While this paves theway for consideration of other ways to generate data, it mainly reduces the VAE framework to anautoencoder while we believe that it can also unveil interesting geometrical aspects.
Another widely discussed improvement of the model consists in trying to tweak the approximateposterior in the ELBO so that it better matches the true posterior using MCMC methods [44] ornormalizing flows [40].
For instance, methods using Hamiltonian equations in the flows to target thetrue posterior [7] were proposed.
Finally, while discussing the potential link between PCA and autoencoders some intuitions aroseon the impact of both the intrinsic structure of the variance of the data [38] and the shape of thecovariance matrices in the posterior distributions [42] on disentanglement in the latent space.
Wealso believe that these covariance matrices indeed play a crucial role in the modeling of the latentspace but in this paper, we instead propose to see their inverse as the value of a Riemannian metric.
Proposed method
In this section, we show that a vanilla VAE unveils naturally a Riemannian structure in its latent spacethrough the learned covariance matrices in the variational posterior distribution.
We then propose anew natural generation scheme guided by this estimated geometry and consisting in sampling from auniform distribution deriving intrinsically from the learned Riemannian manifold.
4.1 A word on Riemannian geometry
First, we briefly recall some basic elements of Riemannian geometry needed in the rest of the paper.
A more detailed discussion on Riemannian manifolds may be found in Appendix A. A d -dimensionalmanifold M is a manifold which is locally homeomorphic to a d -dimensional Euclidean space.
Ifthe manifold M is further differentiable it possesses a tangent space T z at any z ∈ M composed 109 of the tangent vectors of the curves passing by z .
If
M is equipped with a smooth inner product 110 g : z → ⟨·|·⟩ z defined on its tangent space T z for any z ∈ M then M is called a Riemannian 111 manifold and g is the associated Riemannian metric.
Then, a local representation of g at any z ∈ M 112 is given by the positive definite matrix G ( z ) (See Appendix.
A).
If M is connected, a Riemanniandistance between two points z 1 , z 2 of M can be defined
[Equation]
where L is the length of curves γ : R → M traveling from z 1 to z 2 .
Curves minimizing L andparametrized proportionally to the arc length are geodesic .
The manifold M is said to be geodesicallycomplete if all geodesic curves can be extended to R .
In an Euclidean space, G reduces to I d andthe distance becomes the classic Euclidean one.
A simple extension of this Euclidean frameworkconsists in assuming that the metric is given by a constant positive definite matrix Σ differentfrom I d .
In such a case the induced Riemannian distance is the well-known Mahalanobis distance
[Equation]
[Equation]
distribution
Given the Riemannian manifold M endowed with the Riemannian metric G and a chart z , aninfinitesimal volume element may be defined on each tangent space T z of the manifold M as follows
[Equation]
with dz being the Lebesgue measure.
This defines a canonical measure on the manifold and allows toextend the notion of random variables to Riemannian manifolds whose density can be defined withrespect to that Riemannian measure (see Appendix.
A).
Hence, a Riemannian Gaussian distributionon M can be defined using the Riemannian distance of Eq.
(4) instead of the Euclidean one.
[Equation]
where d M z is the volume element defined in Eq.
Thus, a multivariate normal distribution withcovariance matrix Σ is only a specific case of the Riemannian distribution with σ = 1 and defined onthe manifold M =
( R d , G ) where G is the constant Riemannian metric G ( z )
=
Σ − 1 , ∀ z ∈ M .
4.3 Geometrical interpretation of the VAE framework
Within the VAE framework, the variational distribution q ϕ ( z | x ) is voluntarily chosen as a simple multivariate Gaussian distribution defined on R d with d being the latent space dimension.
Hence, as explained in the previous section, given an input data point x i , the posterior q ϕ ( z | x i ) =
N ( µ ( x i ) , Σ ( x i )) can also be seen as a Riemannian Gaussian distribution where the Riemanniandistance is simply the distance with respect to the metric tensor Σ − 1 ( x i ) .
Hence, the VAE framework can be seen as follows.
As with an autoencoder, the VAE provides a code µ ( x i ) which is alower dimensional representation of an input data point x i .
However, it also gives a tensor Σ − 1 ( x i )depending on x i which can be seen as the value of a Riemannian metric G at µ ( x i ) i.e.
[Equation]
This metric is crucial since it impacts the notion of distance in the latent space now seen as the
Riemannian manifold M = ( R d , G ) and so changes the directions that are favored in the samplingfrom the posterior distribution q ϕ
( z | x i ) .
Then, a sample z is drawn from a standard ( i.e. σ = 1 in
Eq.
Riemannian Gaussian distribution and fed to the decoder.
As first approximation and since weonly have access to a finite number of metric tensors Σ − 1 ( x i ) , the VAE model assumes that the metricis locally constant close to µ ( x i ) and so the Riemannian distance reduces to the Mahalanobis distancein the posterior distribution.
This simplifies drastically the training process since now Riemanniandistances have closed form and so are easily computable.
Interestingly, the VAE framework willimpose through the ELBO expression given in Eq.
(3), that z gives a sample x ∼ p θ ( x | z ) close to x iwhen decoded.
Since z has a probability density function imposing higher probability for sampleshaving the smallest Riemannian distance to µ , the VAE imposes in a way that latent variables that areclose in the latent space with respect to the metric G will also provide samples that are close in thedata space X in terms of L2 distance as noticed in Remark.
Noteworthy is that the latter distancecan be amended through the choice of the decoder p θ ( x | z ) .
This is an interesting property since itallows the VAE to directly link the learned Riemannian distance in the latent space to the distance inthe data space.
The regularization term in Eq.
(3) ensures that the covariance matrices do not collapseto
d
and constraints the latent codes to remain close to the origin easing optimization.
Finally, at theend of training, we have a lower dimensional representation of the training data given by the meansof the posteriors µ ( x i ) and a family of metric tensors ( G i =
Σ − 1 ( x i ) )
corresponding to the value ofa
Riemannian metric defined locally on the latent space.
Inspired from [19], we propose to build asmooth continuous Riemannian metric defined on the entire latent space as follows:
[Equation]
where dist Σ − 1 ( x i ) ( z, µ ( x i ))
2 =
( z − µ ( x i ))
⊤ Σ − 1 ( x i )( z − µ ( x i )) is the Riemannian distancebetween z and µ ( x i ) with respect to the locally constant metric G ( µ ( x i ))
=
Σ − 1 ( x i ) .
Since the sumin Eq.
(7) is made on the total number of training samples N , the number of centroids ( µ ( x i ) ) and soof reference metric tensors can be decreased for huge datasets by selecting only k < N elements 1 andincreasing ρ to reduce memory usage.
We provide an ablation study on the impact of λ , the number ofcentroids and their choice along with a discussion on the choice for ρ in
Appendix F.
The parameterτ is only there to ensure that the volume of ( R d , G ) is finite, a property that is needed in Sec.
4.5,and its value can be set as close as desired to zero so the norm of z does not influence the metric close to the centroids.
In practice, it is set below computer precision ( i.e. τ ≈ 0 ).
Rigorously, themetric defined in Eq.
(7) should have been used during the training process.
Nonetheless, this wouldhave made the training longer and trickier since it would involve i) the computation of Riemanniandistances that have no longer closed form and so make the resolution of the optimization problemin Eq.
(4) needed, ii) the sampling from Eq.
(6) which is not trivial and iii) the computation of theregularization term.
Moreover, for small values of β in Eq.
(3), the samples generated from thevariational distribution z ∼ N ( µ ( x i ) , Σ ( x i )) can be assumed to be concentrated around µ ( x i ) andso we have the following first-order Taylor expansion around µ ( x i )
[Equation]
where J ω i ( µ ( x i )) is the Jacobian of the interpolant
ω i evaluated at µ ( x i ) .
Note that we have furtherassumed small enough ρ and λ to neglect the influence of the other Σ ( x j ) in Eq.
Hence byapproximating the value of the metric during training by its value at µ ( x i ) ( i.e. Σ − 1 ( x i ) ), the VAEtraining remains unchanged, stable and computationally reasonable since Riemannian Gaussiansbecome multivariate Gaussians in q ϕ ( z | x ) as explained before.
Noteworthy is the fact that, likewise[18], in our vision of the VAE, the prior distribution is only seen as a regularizer though the KL termand other latent space regularization schemes may have been also envisioned.
In the following, wekeep the proposed vision and do not amend the training.
4.4 Link with the pull-back metric
It has been shown that a natural Riemannian metric on the latent space of generative models canbe the pull-back metric given by G ( z ) =
J g ( z ) ⊤ J g
( z ) [3] and induced by the decoder mappingg : R d → R D outputing the parameters of the conditional distribution p θ ( x | z ) .
Actually, thereexists a strong relation linking the metric proposed in this paper to the pull-back metric.
Indeed,assuming that samples from the variational posterior z ∼ q ϕ ( z | x ) = N ( µ ( x ) , Σ ( x )) remain closeto µ ( x ) ( e.g. by setting a small β in Eq.
(3)) allows to consider an approximation of the log densityh ( z ) :
= log p θ ( x | z ) next to µ ( x ) for a given x [28].
[Equation]
where J h ( µ ( x )) is the Jacobian and H h ( µ ( x )) is the Hessian of h. Using this and remarking that
[Equation]
and E
[Equation]
makes the ELBO in Eq.
(3) write:
[Equation]
Assuming a standard Gaussian prior, Kumar and Poole [28] showed that (cid:101) Σ maximizing the ELBO is
[Equation]
[Equation]
and if we further assume some regularity on the neural networks used for the decoder mapping g ( e.g.piece-wise linear activation functions) we have
[Equation]
where H p ( g ( µ ( x ))) is the Hessian of log p θ ( x ; g ( z )) .
A standard case for the VAE is to assumethat p θ ( x | z ) =
N ( µ θ ( z ) , σ · I D ) and so gives H p ( g ( µ ( x )))
= − 1 σ · I D .
If we further set σ = 1 β ,
Eq.
(11) gives a relation between the pull-back and the metric we propose
[Equation]
Hence, the proposed metric is closely linked to the pull-back metric and may be useful to approximateit (at least close to the µ ( x ) ) and so avoid the computation of a potentially costly function.
− 3 − 2 −   3 −−−
CirclesRings −    (a) −−−   3 −−−
Ours
Afﬁne
Circles
Rings      (b) −−−   3 −−−
Ours
Afﬁne
Circles
Rings
[Figure]
Ours
4.5 Geometry-aware sampling
Assuming that the VAE has learned a latent representation of the data in a space seen as a Riemannianmanifold, we propose to exploit this strong property to enhance the generation procedure.
A naturalway to sample from such a latent space would consist in sampling from the uniform distributionintrinsically defined on the learned manifold.
Similar to the Gaussian distribution presented in
Sec.
4.2, the notion of uniform distribution can indeed be extended to Riemannian manifolds.
Givena set A ⊂ M having a finite volume, a Riemannian uniform distribution on A writes [37]
[Equation]
This density is taken with respect to d M z , the Riemannian measure but using Eq.
(5) and a coordinatesystem z allows to obtain a pdf defined with respect to the Lebesgue one.
Moreover, since the volumeof the whole manifold M = ( R d , G ) is finite, we can now define a uniform distribution on M
[Equation]
Since the Riemannian metric has a closed form expression given by Eq.
(7) sampling from thisdistribution is quite easy and may be performed using the HMC sampler [34] for instance.
Now weare able to sample from the intrinsic uniform distribution which is a natural way of exploring theestimated manifold and the sampling is guided by the geometry of the latent space.
A discussion onpractical outcomes can be found in Appendix.
B.
4.6 Illustration on a toy dataset
The usefulness of such sampling procedure may be easily appended in Figure 1 where a vanilla VAEwas trained with a toy dataset composed of binary images of disks and rings of different size andthickness (example inspired by [9]).
On the left is presented the learned latent space along with theembedded training points given by the colored dots.
The log of the metric volume element is given ingray scale.
In this example, we clearly see a geometrical structure appearing since the disks and ringsseem to wrap around each other.
Obviously, sampling using the prior (taken as a N (0 , I d ) ) in such a case is far from being optimal since the sampling will be performed regardless of the underlyingdistribution of the latent variables and so will create irrelevant samples.
To further illustrate this, wepropose to interpolate between points in the latent space using different cost functions.
Dashed linesrepresent affine interpolations while the solid ones show interpolations aiming at minimizing thepotential V ( z )
= ( (cid:112) det G ( z )) − 1 all along the curve i.e. solving the minimization problem
[Equation]
Below are presented the decoded samples all along the interpolation curves.
Thanks to thoseinterpolations we can see that i) the latent space seems to really have a specific geometrical structuresince decoding all along the interpolation curves obtained by solving Eq.
(12) leads to qualitativelysatisfying results, ii) certain locations of the latent space must be avoided since sampling there willproduce irrelevant samples (see red frames and corresponding red dashes).
Using the proposedsampling scheme will allow to sample in the light-colored areas and so ensure that the samplingremains close to the data i.e. where information is available and so does not produce irrelevant imageswhen decoded while still proposing relevant variations from the input data.
Experiments
In this section, we conduct a comparison with other VAE models using other regularization schemes,more complex priors, richer posteriors, ex-post density estimation or trying to take into accountgeometrical aspects.
In the following, all the models share the same auto-encoding neural networkarchitectures described in Appendix D for a fair comparison.
5.1 Generation with benchmark datasets
First, we compare the proposed sampling method to several VAE variants such as a Wasserstein
Autoencoder (WAE) [47], Regularized Autoencoders [18] with either L2 decoder’s parametersregularization (RAE-L2), gradient penalty (RAE-GP), spectral normalization (RAE-SN) or simple L2latent code regularization (RAE), a vamp-prior VAE (VAMP) [48], a Hamiltonian VAE (HVAE) [7],a geometry-aware VAE (RHVAE) [8] and an Autoencoder (AE).
We elect these models since they usedifferent ways to generate the data using either the prior or ex-post density estimation.
For the latter,we use the approach of [18] and fit a 10-component mixture of Gaussian in the latent space aftertraining.
The models are trained on MNIST [29], SVHN [35], CIFAR 10 [27] and CELEBA [30]and we keep the model achieving the best validation loss.
See Appendix D for the comprehensiveexperimental set-up.
Figure 2 shows a qualitative comparison between the resulting generated samplesfor MNIST and CELEBA, the same plots are made available in Appendix C for SVHN and CIFAR10.
Interestingly, using the non-prior based methods seems to produce qualitatively better samples(rows 7 to end).
Nonetheless, the resulting samples seem even sharper when the sampling takes intoaccount geometrical aspects of the latent space as we propose (last row).
Additionally, even thoughthe exact same model is used, we clearly see that using the proposed method represents a strongimprovement of the generation process from a vanilla VAE when compared to the samples comingfrom a normal prior (second row).
This insists on the fact that even the simplest VAE model actuallycontains a lot of information in its latent space but the limited expressiveness of the prior impedes toaccess to it.
Hence, using more complex priors such as the VAMP may be a tempting idea.
However,one must keep in mind that the ELBO objective in Eq.
(2) must remain tractable and so using moreexpressive priors may be impossible.
These observations are even more supported by Table 1 wherewe report the Frechet Inception Distance (FID) and the precision and recall (PRD) score against thetest set to assess the sampling quality and diversity.
Again, fitting a mixture of Gaussian (GMM)in the latent space appears to be an interesting idea since it allows for a better expressiveness andlatent space prospecting.
For instance, on MNIST the FID falls from 40.7 with the prior to 13.1 whenusing a GMM.
Nonetheless, with the proposed method we are able to make it even smaller (8.5) and PRD scores higher without changing the model and performing post processing.
This can also beobserved on the 3 other datasets.
Impressively, in almost all cases, the proposed generation methodcan either compete or outperform peers both in terms of FID and PRD scores.
Finally, we checkif the proposed method does not overfit the training data and is able to produce diverse samples byshowing the nearest neighbor in the train set and the nearest image in all the reconstructions of the
[Figure]
CELEBA
[Figure]
[Figure] train images to a generated image in Figure 3 (left).
We also provide the FID score between 10kgenerated samples and 10k train reconstructions in Figure 3 (right).
These experiments show that thegenerated samples are not only resampled train images and that the sampling prospects quite well themanifold.
To support even more this claim we provide in Appendix F an analysis in a case whereonly two centroids are selected in the metric.
This also shows that the generated samples are notonly an interpolation between the k selected centroids since some generated images contain attributesthat are not present in the images of the decoded centroids.
The outcome of such an experiment isthat using post training latent space processing such as ex-post density estimation or adding somegeometrical consideration to the model allows to strongly improve the sampling without adding morecomplexity to the model.
Generating 1k samples on CELEBA takes approx.
5.5 min for our methodvs. 4 min for a 10-component GMM and 0.6s for prior based methods on a single GPU V100-16GB.
5.2 Investigating robustness in low data regime
We perform a comparison using the same models and datasets as before but we progressively decreasethe size of the training set to see the robustness of the methods according to the number of samples.
Despite rarely performed in most generative models related papers, this setup appeared to us relevantsince 1) it is well known that it may be challenging for these models, 2) in day-to-day applications
[Table]
0.28/0.49 RAE (L2) 9.
0.35/0.55 RAE (SN) 11. 0.36/0.56 RAE 11.
0.36/0.58 VAE - GMM 13.
VAE - O URS 8.
MNIST
[Figure]
CIFAR0. [Figure] [Figure] [Figure]
[Figure]
[Figure]
(0,1) RAE
[Figure]
[Figure]
[Figure]
[Figure] RAE [Figure] collecting large databases may reveal costly if not impossible ( e.g. medicine).
Hence, we consider
MNIST, CIFAR10 and SVHN and use either the full dataset size, 10k, 5k or 1k training samples.
For each experiment, the best retained model is again the one achieving the best ELBO on thevalidation set the size of which is set as 20% of the train size.
See Appendix D for further detailsabout experiments set-up.
Then, we report the evolution of the FID against the test set in Figure 4.
Results obtained on SVHN are presented in Appendix E. Again, the proposed sampling methodappears quite robust to the dataset size since it outperforms the other models’ FID even when thenumber of training samples is smaller.
This is made possible thanks to the proposed metric thatallows to avoid regions of the latent space having poor information.
Finally, our study shows thatalthough using more complex generation procedures such as ex-post density estimation seems tostill enhance the generation capability of the model when the number of training samples remainsquite high ( ≥ 5k), this gain seems to worsen when the dataset size reduces as illustrated on CIFAR.
Inaddition to those results, we also propose to evaluate the model on a data augmentation tasks withneuroimaging data from OASIS [31] mimicking a day-to-day scenario where the limited data regimeis very common in Appendix.
C.3.
Conclusion
In this paper, we provided a geometric understanding of the latent space learned by a VAE andshowed that it can actually be seen as a Riemannian manifold.
Then, we proposed a new naturalgeneration process consisting in sampling from the intrinsic uniform distribution defined on thislearned manifold.
It showed to be competitive with more advanced versions of the VAEs using eithermore complex priors, ex-post density estimation, normalizing flows or other regularization schemes.
Interestingly, the proposed method revealed good robustness properties in complex settings such ashigh dimensional data or low sample sizes.
Future work would consist in trying to use this method toperform data augmentation in those challenging contexts and compare its reliability for such a taskwith state of the art methods or trying to use this metric to perform clustering in the latent space.
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