Style Memory: Making a Classifier Network Generative
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.
 The paper proposes combining classification-specific neural networks with auto-encoders. This is done in a straightforward manner by designating a few nodes in the output layer for classification and few for reconstruction. The training objective is then changed to minimize the sum of the classification loss (as measured by cross-entropy for instance) and the reconstruction error (as measured by ell-2 error as is done in training auto-encoders). The authors minimize the loss function by greedy layer-wise training as is done in several prior works. The authors then perform other experiments on the learned representations in the output layer (those corresponding to classification + those corresponding to reconstruction). For example, the authors plot the nearest-neighbors for classification-features and for reconstruction-features and observe that the two are very different. The authors also observe that interpolating between two reconstruction-feature vectors (by convex combinations) seems to interpolate well between the two corresponding images. While the experimental results are interesting they are not striking especially when viewed in the context of the tremendous amount of work on auto-encoders. Training the classification-features along with reconstruction-features does not seem to give any significantly new insights.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Style Memory: Making a Classifier Network Generative
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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The paper proposes combining classification-specific neural networks with auto-encoders. This is done in a straightforward manner by designating a few nodes in the output layer for classification and few for reconstruction. The training objective is then changed to minimize the sum of the classification loss (as measured by cross-entropy for instance) and the reconstruction error (as measured by ell-2 error as is done in training auto-encoders). The authors minimize the loss function by greedy layer-wise training as is done in several prior works. The authors then perform other experiments on the learned representations in the output layer (those corresponding to classification + those corresponding to reconstruction). For example, the authors plot the nearest-neighbors for classification-features and for reconstruction-features and observe that the two are very different. The authors also observe that interpolating between two reconstruction-feature vectors (by convex combinations) seems to interpolate well between the two corresponding images. While the experimental results are interesting they are not striking especially when viewed in the context of the tremendous amount of work on auto-encoders. Training the classification-features along with reconstruction-features does not seem to give any significantly new insights.
assistant
 The paper proposes combining classification-specific neural networks with auto-encoders. This is done in a straightforward manner by designating a few nodes in the output layer for classification and few for reconstruction. The training objective is then changed to minimize the sum of the classification loss (as measured by cross-entropy for instance) and the reconstruction error (as measured by ell-2 error as is done in training auto-encoders). The authors minimize the loss function by greedy layer-wise training as is done in several prior works. The authors then perform other experiments on the learned representations in the output layer (those corresponding to classification + those corresponding to reconstruction). For example, the authors plot the nearest-neighbors for classification-features and for reconstruction-features and observe that the two are very different. The authors also observe that interpolating between two reconstruction-feature vectors (by convex combinations) seems to interpolate well between the two corresponding images. While the experimental results are interesting they are not striking especially when viewed in the context of the tremendous amount of work on auto-encoders. Training the classification-features along with reconstruction-features does not seem to give any significantly new insights.