The Variational Homoencoder: Learning to Infer High-Capacity Generative Models from Few Examples
Hierarchical Bayesian methods have the potential to unify many related tasks (e.g. k-shot classification, conditional, and unconditional generation) by framing each as inference within a single generative model. We show that existing approaches for learning such models can fail on expressive generative networks such as PixelCNNs, by describing the global distribution with little reliance on latent variables. To address this, we develop a modification of the Variational Autoencoder in which encoded observations are decoded to new elements from the same class; the result, which we call a Variational Homoencoder (VHE), may be understood as training a hierarchical latent variable model which better utilises latent variables in these cases. Using this framework enables us to train a hierarchical PixelCNN for the Omniglot dataset, outperforming all existing models on test set likelihood. With a single model we achieve both strong one-shot generation and near human-level classification, competitive with state-of-the-art discriminative classifiers. The VHE objective extends naturally to richer dataset structures such as factorial or hierarchical categories, as we illustrate by training models to separate character content from simple variations in drawing style, and to generalise the style of an alphabet to new characters.
 The paper presents some conceptually incremental improvements over the models in “Neural Statistician” and “Generative matching networks”. Nevertheless, it is well written and I think it is solid work with reasonable convincing experiments and good results. Although, the authors use powerful PixelCNN priors and decoders and they do not really disentangle to what degree their good results rely on the capabilities of these autoregressive components.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
The Variational Homoencoder: Learning to Infer High-Capacity Generative Models from Few Examples
Hierarchical Bayesian methods have the potential to unify many related tasks (e.g. k-shot classification, conditional, and unconditional generation) by framing each as inference within a single generative model. We show that existing approaches for learning such models can fail on expressive generative networks such as PixelCNNs, by describing the global distribution with little reliance on latent variables. To address this, we develop a modification of the Variational Autoencoder in which encoded observations are decoded to new elements from the same class; the result, which we call a Variational Homoencoder (VHE), may be understood as training a hierarchical latent variable model which better utilises latent variables in these cases. Using this framework enables us to train a hierarchical PixelCNN for the Omniglot dataset, outperforming all existing models on test set likelihood. With a single model we achieve both strong one-shot generation and near human-level classification, competitive with state-of-the-art discriminative classifiers. The VHE objective extends naturally to richer dataset structures such as factorial or hierarchical categories, as we illustrate by training models to separate character content from simple variations in drawing style, and to generalise the style of an alphabet to new characters.
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 presents some conceptually incremental improvements over the models in “Neural Statistician” and “Generative matching networks”. Nevertheless, it is well written and I think it is solid work with reasonable convincing experiments and good results. Although, the authors use powerful PixelCNN priors and decoders and they do not really disentangle to what degree their good results rely on the capabilities of these autoregressive components.
assistant
 The paper presents some conceptually incremental improvements over the models in “Neural Statistician” and “Generative matching networks”. Nevertheless, it is well written and I think it is solid work with reasonable convincing experiments and good results. Although, the authors use powerful PixelCNN priors and decoders and they do not really disentangle to what degree their good results rely on the capabilities of these autoregressive components.