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Title: Taking Apart Autoencoders: How do They Encode Geometric Shapes ?

Paper content:
We study the precise mechanisms which allow autoencoders to encode and decode a simple geometric shape, the disk.
In this carefully controlled setting, we are able to describe the specific form of the optimal solution to the minimisation problem of the training step.
We show that the autoencoder indeed approximates this solution during training.
Secondly, we identify a clear failure in the generalisation capacity of the autoencoder, namely its inability to interpolate data.
Finally, we explore several regularisation schemes to resolve the generalisation problem.
Given the great attention that has been recently given to the generative capacity of neural networks, we believe that studying in depth simple geometric cases sheds some light on the generation process and can provide a minimal requirement experimental setup for more complex architectures.
Autoencoders are neural networks, often convolutional neural networks, whose purpose is twofold.
Firstly, to compress some input data by transforming it from the input domain to another space, known as the latent, or code, space.
The second goal of the autoencoder is to take this latent representation and transform it back to the original space, such that the output is similar, with respect to some criterion, to the input.
One of the main objectives of this learning process being to reveal important structure in the data via the latent space, and therefore to represent this data in a more meaningful fashion or one that is easier to model.
Autoencoders have been proven to be extremely useful in many tasks ranging from image compression to synthesis.
Many variants on the basic idea of autoencoders have been proposed, the common theme being how to impose useful properties on the learned latent space.
However, very little is known about the actual inner workings and mechanisms of the autoencoder.The goal of this work is to investigate these mechanisms and describe how the autoencoder functions.
Many applications of autoencoders or similar networks consider relatively high-level input objects, ranging from the MNIST handwritten digits to abstract sketches of conceptual objects BID18 ; BID7 ).
Here, we take a radically different approach.
We consider, in depth, the encoding/decoding processes of a simple geometric shape, the disk, and investigate how the autoencoder functions in this case.
There are several important advantages to such an approach.
Firstly, since the class of objects we consider has an explicit parametrisation, it is possible to describe the "optimal" performance of the autoencoder, ie.
can it compress and uncompress a disk to and from a code space of dimensionality 1 ?
Secondly, the setting of this study fixes certain architecture characteristics of the network, such as the number of layers, leaving fewer free parameters to tune.
This means that the conclusions which we obtain are more likely to be robust than in the case of more high-level applications.
Finally, it is easier to identify the roles of different components of the network, which enables us to carry out an instructive ablation study.Using this approach, we show that the autoencoder approximates the theoretical solution of the training problem when no biases are involved in the network.
Secondly, we identify certain limitations in the generalisation capacity of autoencoders when the training database is incomplete with respect to the underlying manifold.
We observe the same limitation using the architecture of BID18 , which is considerably more complex and is proposed to encode natural images.
Finally, we analyse several regularisation schemes and identify one in particular which greatly aids in overcoming this generalisation problem.
We have investigated in detail the specific mechanisms which allow autoencoders to encode image information in an optimal manner in the specific case of disks.
We have shown that, in this case, the encoder functions by integrating over disk, and so the code z represents the area of the disk.
In the case where the autoencoder is trained with no bias, the decoder learns a single function which is multiplied by scalar depending on the input.
We have shown that this function corresponds to the optimal function.
The bias is then used to induce a thresholding process applied to ensure the disk is correctly decoded.
We have also illustrated certain limitations of the autoencoder with respect to generalisation when datapoints are missing in the training set.
This is especially problematic for higher-level applications, whose data have higher intrinsic dimensionality and therefore are more likely to include such "holes".
Finally, we identify a regularisation approach which is able to overcome this problem particularly well.
This regularisation is asymmetrical as it consists of regularizing the encoder while leaving more freedom to the decoder.An important future goal is to extend the theoretical analyses obtained to increasingly complex visual objects, in order to understand whether the same mechanisms remain in place.
We have experimented with other simple geometric objects such as squares and ellipses, with similar results in an optimal code size.
Another question is how the decoder functions with the biases included.
This requires a careful study of the different non-linearity activations as the radius increases.
Finally, the ultimate goal of these studies is to determine the capacity of autoencoders to encode and generate images representing more complex objects or scenes.
As we have seen, the proposed framework can help identifying some limitations of complex networks such as the one from BID18 and future works should investigate whether this framework can help developing the right regularization scheme or architecture.
Value of < f, 1 Br >, plotted against z Figure 7 : Verification of the hypothesis that y(t, r) = h(r)f (t) for decoding in the case where the autoencoder contains no bias..
We have determined the average profile of the output of the autoencoder when no biases are involved. On the left, we have divided several random experimental profiles y by the function h, and plotted the result, which is close to constant (spatially) for a fixed radius of the input disk. On the right, we plot z against the theoretically optimal value of h (C f, 1 Br , where C is some constant accounting for the arbitrary normalization of f ). This experimental sanity check confirms our theoretical derivations.

assistant: text: We study the functioning of autoencoders in a simple setting and advise new strategies for their regularisation in order to obtain bettre generalisation with latent interpolation in mind for image sythesis.