Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Deep networks have shown great performance in classification tasks.
However, the parameters learned by the classifier networks usually discard stylistic information of the input, in favour of information strictly relevant to classification.
We introduce a network that has the capacity to do both classification and reconstruction by adding a "style memory" to the output layer of the network.
We also show how to train such a neural network as a deep multi-layer autoencoder, jointly minimizing both classification and reconstruction losses.
The generative capacity of our network demonstrates that the combination of style-memory neurons with the classifier neurons yield good reconstructions of the inputs when the classification is correct.
We further investigate the nature of the style memory, and how it relates to composing digits and letters.
Deep neural networks now rival human performance in many complex classification tasks, such as image recognition.
However, these classification networks are different from human brains in some basic ways.
First of all, the mammalian cortex has many feed-back connections that project in the direction opposite the sensory stream BID1 .
Moreover, these feed-back connections are implicated in the processing of sensory input, and seem to enable improved object/background contrast BID10 , and imagination BID11 .
Feed-back connections are also hypothesized to be involved in generating predictions in the service of perceptual decision making BID14 .Humans
(and presumably other mammals) are also less susceptible to being fooled by ambiguous or adversarial inputs. Deep neural
networks have been shown to be vulnerable to adversarial examples BID15 BID3 . Slight modifications
to an input can cause the neural network to misclassify it, sometimes with great confidence! Humans do not get fooled
as easily, leading us to wonder if the feed-back, generative nature of real mammalian brains contributes to accurate classification.In pursuit of that research, we wish to augment classification networks so that they are capable of both recognition (in the feed-forward direction) and reconstruction (in the feed-back direction). We want to build networks
that are both classifiers and generative.The nature of a classifier network is that it throws away most of the information, keeping only what is necessary to make accurate classifications. Simply adding feed-back connections
to the network will not be enough to generate specific examples of the input -only a generic class archetype. But what if we combine the features
of a classifier network and an autoencoder network by adding a "style memory" to the top layer of the network? The top layer would then consist of
a classification component as well as a collection of neurons that are not constrained by any target classes.We hypothesized that adding a style memory to the top layer of a deep autoencoder would give us the best of both worlds, allowing the classification neurons to contribute the class of the input, while the style memory would record additional information about the encoded input -presumably information not encoded by the classification neurons. The objective of our network is to
minimize both classification and reconstruction losses so that the network can perform both classification and reconstruction effectively. As a proof of concept, we report on
a number of experiments with MNIST and EMNIST that investigate the properties of this style memory.
Classification networks do not typically maintain enough information to reconstruct the input; they do not have to.
Their goal is to map high-dimensional inputs to a small number of classes, typically using a lower-dimensional vector representation.
In order for a classification network to be capable of generating samples, additional information needs to be maintained.
In this paper, we proposed the addition of "style memory" to the top layer of a classification network.
The top layer is trained using a multi-objective optimization, trying to simultaneously minimize classification error and reconstruction loss.(a
) (Figure 11: Image reconstruction with style memory interpolation between digits and letters shown in FIG7 and FIG0 , where λ was increasing from 0.1 to 1.0 with a step of 0.1 from top to bottom.Our experiments suggest that the style memory encodes information that is largely disjoint from the classification vector. For
example, proximity in image space yields digits that employ an overlapping set of pixels. However
, proximity in style-memory space yielded a different set of digits.For the style interpolation experiment, we generated images from a straight line in style-memory space. However
, each position on this line generates a sample in image space -an image; it would be interesting to see what shape that 1-dimensional manifold takes in image space, and how it differs from straight-line interpolation in image space. However
, the fact that we were able to interpolate digits and letters within the same class using novel style-memory activation patterns suggests that the style memory successfully encodes additional, abstract information about the encoded input.To our knowledge, existing defence mechanisms to combat adversarial inputs do not involve the generative capacity of a network. Motivated
by the results in Sec. 4.1, preliminary experiments that we have done suggest that treating perception as a two-way process, including both classification and reconstruction, is effective for guarding against being fooled by adversarial or ambiguous inputs. Continuing
in this vein is left for future work.Finally, we saw that the network has a property where the reconstruction generated was affected both by the classification neurons and style memory. Inspired by
how human perception is influenced by expectation BID14 , we believe that this work opens up opportunities to create a classifier network that takes advantage of its generative capability to detect misclassifications. Moreover, predictive
estimator networks might be a natural implementation for such feed-back networks BID17 BID14 BID9 . Perception and inference
could be the result of running the network in feed-forward and feed-back directions simultaneously, like in the wake-sleep approach BID5 . These experiments are ongoing
.
Augmenting the top layer of a classifier network with a style memory enables it to be generative.