Category Archives: Autoencoder

Autoencoders and latent space fragmentation – VI – image creation from z-points along paths in selected coordinate planes of the latent space

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It is well known that standard (convolutional) Autoencoders [AEs] cause problems when you want to use them for creative purposes. An example: Creating images with human faces by feeding the Decoder of a suitably trained AE with random latent vectors does not work well. In this series of posts I want to identify the cause of this specific problem. Another objective is to circumvent some of the related obstacles and create reasonably clear images nevertheless. Note that I speak about standard Autoencoders, not Variational Autoencoders or transformer based Encoder/Decoder-systems. For basic concepts, terms and methods see the previous posts:

Autoencoders and latent space fragmentation – I – Encoder, Decoder, latent space
Autoencoders and latent space fragmentation – II – number distributions of latent vector components
Autoencoders and latent space fragmentation – III – correlations of latent vector components
Autoencoders and latent space fragmentation – IV – CelebA and statistical vector distributions in the surroundings of the latent space origin
Autoencoders and latent space fragmentation – V – reconstruction of human face images from simple statistical z-point-distributions?

So far I have demonstrated that randomly generated vectors most often do not hit the relevant regions in the AE’s latent space – if we do not take some data specific precautions. A relevant region is a confined volume which a trained Decoder fills with z-points for its training objects after the training has been completed. z-points and corresponding latent vectors are the result of an encoding process which maps digitized input objects into the latent space. Depending on the data objects we may get multiple relevant regions or just one compact region. In the case of a convolutional AE which I had trained with the CelebA dataset of human face images I found single region with a rather compact core.

In this post I want to create statistical latent vectors whose end-points are located inside the relevant region for CelebA images. Then I will create images from such latent vectors with the help of the AE’s Decoder. My hope is to get at least some images with clearly visible human faces. The basic idea behind this experiment is that the most important features of human faces are encoded by a few dominant vector components defining the overall position and shape of the multidimensional z-point region for CelebA images. We will see that the theory is indeed valid: Here is a first example for a vector pointing to an outer area of the core region for CelebA images in the latent space:

Our AE is a convolutional one. The number of latent space dimensions N was chosen to be N=256.
Note: We are NOT using a Variational Autoencoder, but a simple standard Autoencoder. The AE’s properties were discussed in previous posts.

What have we found out so far?

The Encoder of the convolutional AE, which I had trained with the CelebA dataset, mapped the human face images into a compact region of the latent space. The core of the created z-point distribution was located within or very close to a tiny hyper-volume of the latent space spanned by only a few coordinate axes. The confined multi-dimensional volume occupied by most of the z-points had an overall ellipsoidal shape with major extensions along a few main axes. We saw that some of the coordinates of the CelebA z-points and the components of the corresponding latent vectors were strongly correlated. In addition the value range of each of the latent vector components had specific individual limits – confining the angles and lengths of the vectors for CelebA. Therefore we had to conclude:

Whenever we base our method to create statistical vectors on the assumptions

  • that one can treat the vector components as independent statistical variables
  • that one can assign statistical values to the components from a common real value interval

the vectors will almost certainly not point to the relevant region. In addition one has to take into account unexpected mathematical properties of statistical vector distributions in high dimensional spaces. See the previous posts for more details. Indeed we could show that such a vector generation method missed the CelebA region.

Objective of this post

In this post I want to use some of the knowledge which we have gathered about the latent vector distribution for CelebA images. We shall use a very simple approach to probe the image reconstruction abilities of the Decoder for a defined variety of z-points:

We restrict the vectors’ component values such that most of the vectors point to the region formed by the bulk of CelebA z-points. To achieve this we define straight line segments which cross the ellipsoidal region of CelebA z-points. This is possible due to the known value intervals which we have identified for each of the components in a previous post. Then we place some artificial z-points onto our line segments. At least some of these z-points will fall into the relevant CelebA region. We then let the Decoder reconstruct images for the latent vectors corresponding to these z-points.

In some cases our paths will even respect some major component correlations, but for some paths I will explicitly disregard such correlations. Nevertheless our rather simple restrictions imposed on the vector-component values will already enable us to produce images with clearly recognizable face features.

Among other things our results confirm the idea that the real pixel correlations for basic face features are represented by relatively narrow limits for the angles and lengths of respective latent vectors. The extension and shape of the bulk region of CelebA z-points is defined by only a few latent vector components. These components apparently encode a prescription for the (convolutional) Decoder to create face features by a superposition of some elementary patterns extracted during the AE’s training.

A path from the latent space origin to the center of the relevant z-point region

How do we restrict latent vectors to the required value ranges? In the 2nd post we have seen that the number distribution curve for the values of each of the latent vector components was very similar to a Gaussian. We have identified the mean value and average value range for each component by analyzing its specific distribution curve. The mean values gave us the coordinates of the center of the relevant latent space region. In addition we, of course, know the coordinates of the origin of the latent space. So, for a first test, let us create a multi-dimensional line segment between the origin and the center of the CelebA z-point distribution. And let the A’s Decoder create images for latent vectors pointing to some intermediate z-points along this path.

The following plots show orthogonal projections of 5000 CelebA z-points (in blue) onto some 2-dimensional planes spanned by two selected coordinate axes. The yellow dot indicates the origin. The orange dot the center of the z-point distribution. Red dots indicate coordinates of points along the straight path between the origin and the distribution center.

Please, take note of the different scales on the x- and y-axes. Some distributions are much more elongated than the scaled images show. That some paths appear shorter than others is due to the projection of the diagonal line through the multi-dimensional space onto planes which are differently oriented with respect to this line. A simple 3D analog should make this clear. Some small wiggles in the positions of the red dots are due to resolution problems of the plot on the browser interface. We also see a reflection of the fact that the origin is located in a border region of the bulk.

Below you see a plot which shows the path in higher resolution (projected onto a particular plane):

Again: Take note of the different axis scales. The blue dot distribution is much more stretched in C1-direction than it appears in the plot.

Ok, now we have a multidimensional path and six well defined latent vectors for the end and intermediate points on this path. So let us provide these vectors as input to the our AE’s Decoder. The resulting images look like:

Success! Images in the surroundings of the center show a clearly visible face. And we also see: The average face at the center of the z-point distribution is female – at least according to the CelebA dataset. 🙂 However: In the vicinity of the origin of the latent space we get no images with reasonable face features.

Images along a path within a selected coordinate plane for two dominant vector components

I choose a different path within the plane spanned by the coordinates axes 151 and 195 now. This is depicted in the plot below:

A look into the second post shows you that the components 151, 195 were members of the group of dominant components. Those were components for which the number distribution showed a mean value at some distance from the origin of the latent space and also had a half-width bigger than 1.0 (as most of the other components). The images reconstructed by the Decoder from the latent vectors are:

Hey, we get some variation – as expected. Now, let us rotate the path in the plane:

Not so much of a difference. But we have learned that a variation of some vector component values within the allowed range of values may give us already some major variation in the faces’ expressions.

Images for other coordinate planes

The following images show the variations for paths in other coordinate planes. All of the paths have in common that they pass the center of the CelebA bulk region. For the first 4 examples I have kept the path within the core region of CelebA z-points. The last images show images for paths with z-points at the core’s border regions or a bit outside of it.

Plane axes: 5, 8

Plane axes: 17, 180

Plane axes: 44, 111

Plane axes: 55, 56

Plane axes: 15, 242

Plane axes: 58 202

Plane axes: 68, 178

Plane axes: 177, 202

Plane axes: 180, 242

The images for z-points farther away from the bulk’s center give you more interesting variations. But obviously in the outer areas of the CelebA region correlations between the latent vector components get more important when we want to avoid irregular and unrealistic disturbances. All in all we also get the impression that a much more subtle correlation of component values is a key for the reproduction of realistic transitions for the hairdos presented in the CelebA images and the transition to some realistic background patterns. The components of our latent vectors are still too uncorrelated for such details and an appropriate superposition of micro-patterns in the images created by the Decoder.

Conclusion

This blog shows that we do not need a Variational Autoencoder to produce images with recognizable human faces from statistical latent vectors. We can get image reproductions with varying face features also from the Decoder of a standard convolutional Autoencoder. A basic requirement seems to be that we keep the vector components within reasonable value intervals. The valid component specific value ranges are defined by the shape of the compact hyper-volume, which an AE’s Encoder fills with z-points for its training objects. So we need to construct statistical latent vectors which point to this specific sub-region of the latent space. Vectors with arbitrary components will almost certainly miss this region and give no interpretable image content.

In this post we have looked at vectors defining z-points along specific line segments in the latent space. Some of the paths were explicitly kept within the inner core regions of the z-point-distribution for CelebA images. From these z-points the most important face features were clearly reconstructed. But we also saw that some micro-correlations of the latent vector components seem to control the appearance of the background and the transition from the face to hair and from the hair to the background-environment.

I have not yet looked at line segments which do not cross the center of the bulk of the z-point distribution for CelebA images in the latent space. But in the next post I first want to look at z-points for which we relatively freely vary the component values within ranges given by the respective number distributions.

 

Autoencoders and latent space fragmentation – V – reconstruction of human face images from simple statistical z-point-distributions?

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In this post series we have so far studied the distribution of latent vectors and z-points for CelebA images in the latent space of an Autoencoder [AE]. The CelebA images show human faces. We want to reconstruct images with new faces from artificially created, statistical latent vectors. Our latent space had a number dimensions N=256. For basics of Autoencoders, terms like latent space, z-points, latent vectors etc. see the first blog of this series:

Autoencoders and latent space fragmentation – I – Encoder, Decoder, latent space

During the experiments and analysis discussed in the other posts

Autoencoders and latent space fragmentation – II – number distributions of latent vector components
Autoencoders and latent space fragmentation – III – correlations of latent vector components
Autoencoders and latent space fragmentation – IV – CelebA and statistical vector distributions in the surroundings of the latent space origin

we have learned that the multi-dimensional latent space volume which the Encoder of a convolution AE fills densely with z-points for CelebA images has a special shape and location. The bulk of z-points is confined to a multi-dimensional ellipsoid which is relatively small. Its center has a position, which does not coincide with the latent space’s origin. The bulk center is located within or very close to a hyper-sub-volume spanned by only a few coordinate axes.

We also saw also that we have difficulties to hit this region by artificially created z-points via latent vectors. Especially, if the approach for statistical vector generation is a simple one. In the beginning we had naively assumed that we can treat the vector components as independent variables. We assigned each vector component values, which we took from a value-interval [-b, b] with a constant probability density. But we saw that such a simple and seemingly well founded method for statistical vector creation has a variety of disadvantages with respect to the bulk distribution of latent vectors for CelebA images. To name a few problems:

  • The components of the latent vectors for CelebA images are correlated and not independent. See the third post for details.
  • If we choose b too large (&gt. 2.0) then the length or radius values of the created vectors will not fit typical radii of the CelebA vectors. See the 2nd post for details.
  • The generated vectors only correspond to z-points which fill parts of a thin multi-dimensional spherical shell within the cube filled by points with coordinate values -b < x_j < +b. This is due to mathematical properties of the distribution in multi-dimensional spaces. See the second post for more details.
    • Optimal parameter values are 1.0 < b < 2.0, just to guarantee at least the right vector length. However, due to the fact that the origin of the latent space is located in a border region of the CelebA bulk z-point distribution, most points will end up outside the bulk volume.

In this post I will show you that our statistical vectors and the related z-points do indeed not lead to a successful reconstruction of images with clearly visible human faces. Afterward I will briefly discuss whether we still can have some hope to create new human face images by a standard AE’s Decoder and statistical points in its latent space.

Relevant values of parameter b for the interval from which we choose statistical values for the vector components

What does the number distribution for the length of latent CelebA vectors look like? For case I explained in the 2nd post of this series we get:

For case II:

Now let me remind you of a comparison with the lengths of statistical vectors for different values of b:

See the 2nd post of this series for more details. Obviously, for our simple generation method used to create statistical vectors a parameter value b = 1.5 is optimal. b=1.0 an b=2.0 mark the edges of a reasonable range of b-values. Larger or smaller values will drive the vector lengths out of the required range.

Image reconstructions for statistical vectors with independent component values x_j in the range -1.5 < x_j < 1.5

I created multiples of 512 images by feeding 512 statistical vectors into the Decoder of our convolutional Autoencoder which was trained on CelebA images (see the 1st and the 2nd post for details). The vector components x_j fulfilled the condition :

-1.5 ≤   x_j   ≤ 1.5,    for all j in [0, 256]

The result came out to be frustrating every time. Only singular image showed like outlines of a human face. The following plot is one example out of series of tenths of failures.

And this was an optimal b-value! 🙁 But due to the analysis of the 4th post in this series we had to expect this.

Image reconstructions for statistical vectors with independent component values x_j in the range -1.0 < x_j < 1.0

The same for

-1.0 ≤   x_j   ≤ 1.0,    for all j in [0, 256]

Image reconstructions for statistical vectors with independent component values x_j in the range -2.0 < x_j < 2.0

The same for

-2.0 ≤   x_j   ≤ 2.0,    for all j in [0, 256]

Image reconstructions for statistical vectors with independent component values in the range -5.0 < x_j < 5.0

If you have read the previous posts in this series then you may think: Some of the component values of latent vectors for CelebA images had bigger values anyway. So let us try:

-1.0 ≤   x_j   ≤ 1.0,    for all j in [0, 256]

And we get:

Although some component values may fall into the value regions of latent CelebA vectors most of them do not. The lengths (or radii) of the statistical vectors for b=5.0 do not at all fit the average radii of latent vectors for CelebA images.

What should we do?

I have described the failure to create reasonable images from statistical vectors in some regions around the origin of the latent space also in other posts on standard Autoencoders and in posts on Variational Autoencoders [VAEs]. For VAEs one enforces that regions around the origin are filled systematically by special Encoder layers.

But is all lost for a standard Encoder? No, not at all. It is time that we begin to use our knowledge about the latent z-point distribution which our AE creates for CelebA images. As I have shown in the 2nd post it is simple to get the number distribution for the component values. Because these distributions were similar to Gaussians we have rather well defined value regions per component which we can use for vector creation. I.e. we can perform a kind of first order approximation to the main correlations of the components and thus put our artificially created values inside the densely populated bulk of the z-point region for CelebA images. If that region in the latent space has a meaning at all then we should find some interesting reconstruction results for z-points within it.

We can do this even with our simple generation method based on constant probability densities, if we use individual value regions -b_j ≤ x_j ≤ b_j for each component. (Instead of a common value interval for all components). But even better would be Gaussian approximations to the real number distributions for the component values. In any case we have to restrict the values for each component much stronger than before.

Conclusion

What we already had expected from previous analysis became true. A simple method for the creation of statistical latent vectors does not give us z-points which are good for the creation of reasonable images by a trained AE’s Decoder. The simplifications

  1. that we can treat the component values of latent vectors as independent variables
  2. and that we can assign the components x_j values from a common interval -b ≤ x_j &le b

cause that we miss the bulk region in the AE’s latent space, which gets filled by its trained Encoder. We have demonstrated this for the case of an AE which had been trained on images of human faces.

In the next post

Autoencoders and latent space fragmentation – VI – image creation from z-points along paths in selected coordinate planes of the latent space

I will show that even a simple vector creation can give us latent vectors within the region filled for CelebA images. And we will see that such points indeed lead to the reconstruction of images with clearly visible human face images by the Decoder of a standard AE trained on the CelebA dataset.

 

Autoencoders and latent space fragmentation – IV – CelebA and statistical vector distributions in the surroundings of the latent space origin

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I continue with my investigation of the z-point- and latent vector distribution which a convolutional Autoencoder [AE] creates in its latent space for CelebA images. Such images show human faces – and our objective is to find out whether we can force the AE’s Decoder to create human face images from artificially generated and statistically distributed z-points in the latent space. E.g. for creative tasks – without using a Variational Autoencoder.

The first posts of this series

Autoencoders and latent space fragmentation – I – Encoder, Decoder, latent space
Autoencoders and latent space fragmentation – II – number distributions of latent vector components
Autoencoders and latent space fragmentation – III – correlations of latent vector components

have revealed that the multi-dimensional volume region filled with z-points for CelebA images is rather small and has an ellipsoidal shape. The region is extended in the direction of a few main axes. Its center is located at some distance from the origin of the latent space. Its position is rather close to or within a hyper-volume of the latent space spanned by a few axes, only. The origin of the latent space is instead located close to the border of the bulk region of CelebA z-points.

We have also found out that artificially created z-points may miss the region of the CelebA z-points. In particular when we generate respective vectors under the assumption that the vector components are independent variables and can be filled with values obeying a constant probability distribution within a real value interval [-b, b]. See the second post for links to a study of the mathematical properties of such artificial vector distributions. We saw that the radii of the artificial vectors only match those of CelebA vectors if we choose 1.0 < b < 2.0. An optimal value appeared to be b = 1.5. This means that the created statistical vectors would have positions relatively close to the origin. We had hoped that such artificial vectors overlap at least in parts with the latent vector distribution for CelebA. Such an overlap may be required to get a reconstruction of images with clearly visible human faces.

In this post I, therefore, have a look at the surroundings of the latent space origin. We focus on projections of the neighboring z-points onto planes formed by selected latent vector components. We choose these components such that the border position of the origin with respect to the volume occupied by the bulk of CelebA z-points becomes clear. We afterward look at real and artificial z-points close to a slice of the multi-dimensional latent space volume. The vectors to the z-points in this slice fulfill the following condition: All components x_j, with the exception of two selected ones, have values x_j < 1.5. This will reduce projection effects with respect to the selected projection plane. The results will show us that many of the artificial z-points unfortunately fall into empty regions (voids). It is sufficient to show this for some selected coordinate pairs. The latent space of our AE has N=256 dimensions.

Position of the origin with respect to the CelebA z-point distribution

First I want to remind you of the border position of the latent space’s origin with respect to the bulk of the CelebA z-point-distribution. The following plots show again 5000 randomly selected z-points corresponding to latent vectors for CelebA images (blue points). The yellow point marks the origin of the latent space. The red dots correspond to 10 artificially created z-points for b = 1.5. The individual plots correspond to selected pairs of vector components and planes spanned by respective axes.

That the center of the distribution appears extremely densely populated is a bit due to the chosen diameter of the blue points. When interpreting these plots, please note: We are looking at orthogonal projections. Therefore we always have to take into account projection effects.

A closer look at the environment of the latent space’s origin

The following plot shows the environment of the origin with a higher resolution for our 5600 z-points. Despite the fact that this is a projection of many points onto the selected plane we get a first impression that CelebA z-point distribution is not really a homogeneous one – although being a relatively dense one around the center of the ellipsoidal bulk distribution.

Some of our artificial z-points seem in both cases to mix with the CelebA z-points. Below I want to show that this is a projection effect, only.

The surroundings of the origin in a flat cuboid

In the second post of this series we had derived that a parameter b = 1.5 is optimal to get the right vector length of our artificial statistical vectors to match the length of the latent CelebA vectors. Therefore, I have reduced the amount of CelebA z-points by imposing the following conditions on the components x_j:

-1.5 ≤   x_j   ≤ 1.5,    for all j in [0, 256], with the exception of two selected values j = j1 or j = j2

I.e. we look at CelebA z-points close to the plane defined by the axes corresponding to our specially selected vector components x_j1 and x_j2. Thus we get rid of projection effects from any points outside the multi-dimensional slice. We only get projections from points inside our multi-dimensional slice, which contains the cube defined by a side-length -1.5 ≤ x_j ≤ +1.5 around the origin. Our statistically generated vectors have end-points inside this multi-dimensional cube. The result is:

Ooops, only two out of our 5000 CelebA points are present in the slice region, which I also have populated with 200 artificial z-points. So, clearly this is not a region which the AE’s Encoder fills densely for CelebA images.

Even for 80,000 CelebA z-points the situation does not improve so much. Only 56 latent CelebA vectors point to our region.

Most of the artificially created z-points (in red) thus come to fall into empty volume regions – regions not used by CelebA z-points. This already diminishes our chances to reconstruct reasonable human face images by our artificial distribution of latent vectors.

Situation for a second and a third plane

Can we reproduce this also for other component pairs? Yes, indeed, e.g. for the pair (177, 242):

For 5000 CelebA z-points:

Only one out of 5000 CelebA vectors points to the relevant slice:

For 80,000 images 39 regular CelebA z-points survive, only. I skip the respective image.

Vector components (30, 118)
Another interesting pair of components and respective coordinate axes is (30, 118):

And for our slice we get:

From 80,000 points only around 70 are located in our slice of the multidimensional space:

Vector components (118, 156)
For the pair (118, 156) the respective plots are:

We see some overlaps between the artificially created points and the CelebA z-points. However, you should keep in mind that the probability that an artificial point falls into a void in the multi-dimensional space gets bigger with every individual component value putting the point outside the CelebA bulk region. And: Our “overlaps” are still the result of a (significantly reduced) projection effect. Furthermore, the plots do not distinguish the components of an individual point from those of other points. If one component shows an overlap with CelebA points, another component for the same point may not. And one component is enough to determine a position outside the bulk.

Radii of the artificially created z-points

When rating probabilities of our artificially created z-points to hit a region populated by CelebA z-points you should also remember that our artificially created points fall into a rather narrow spherical shell for so many dimensions as our latent space has. See the second post of this series for this phenomenon.

Conclusion

What have we learned? The second post in this series gave us hope that at least some of the artificially created z-points (based on independent component values taken with a constant probability from a common value interval) would get a position within the confined region populated by the real CelebA z-points. A closer look, however, showed us that the origin of the latent space resides within a border-region of the ellipsoidal bulk of the multi-dimensional CelebA z-point distribution. Only very few CelebA z-points are found in this border region and within slices close to selected coordinate planes.

What does this mean? The chances that most of the artificially created z-points for b = 1.5 will fall into a void not used by the AE’s Decoder for CelebA images is much bigger than we originally may have thought. In addition our statistical points only populate a spherical shell within a multi-dimensional cube around the origin of the latent space with a side length of 2b. Even if we compensate this effect by generating vectors for different b-values we do not gain much. This raises the fundamental question whether a method that generates statistical z-points via independent component values is a reasonable choice for our objective to reconstruct human face images.

In the next post

Autoencoders and latent space fragmentation – V – reconstruction of human face images from simple statistical z-point-distributions?

I will show that the results of such reconstruction efforts are indeed frustrating. As a consequence I will discuss how we could simply adjust our generating method to the real distribution of latent vectors for CelebA images.