PixelVAE: A Latent Variable Model for Natural Images
Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and model global structure well but have difficulty capturing small details. PixelCNN models details very well, but lacks a latent code and is difficult to scale for capturing large structures. We present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. Our model requires very few expensive autoregressive layers compared to PixelCNN and learns latent codes that are more compressed than a standard VAE while still capturing most non-trivial structure. Finally, we extend our model to a hierarchy of latent variables at different scales. Our model achieves state-of-the-art performance on binarized MNIST, competitive performance on 64 × 64 ImageNet, and high-quality samples on the LSUN bedrooms dataset.
The paper combines a hierarchical Variational Autoencoder with PixelCNNs to model the distribution of natural images. They report good (although not state of the art) likelihoods on natural images and briefly start to explore what information is encoded by the latent representations in the hierarchical VAE. I believe that combining the PixelCNN with a VAE, as was already suggested in the PixelCNN paper, is an important and interesting contribution. The encoding of high-, mid- and low-level variations at the different latent stages is interesting but seems not terribly surprising, since the size of the image regions the latent variables model is also at the corresponding scale. Showing that the PixelCNN improves the latent representation of the VAE with regard to some interesting task would be a much stronger result. Also, while the paper claims, that combining the PixelCNN with the VAE reduces the number of computationally expensive autoregressive layers, it remains unclear how much more efficient their whole model is than an PixelCNN with comparable likelihood. In general, I find the clarity of the presentation wanting. For example, I agree with reviewer1 that the exact structure of their model remains unclear from the paper and would be difficult to reproduce.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
PixelVAE: A Latent Variable Model for Natural Images
Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and model global structure well but have difficulty capturing small details. PixelCNN models details very well, but lacks a latent code and is difficult to scale for capturing large structures. We present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. Our model requires very few expensive autoregressive layers compared to PixelCNN and learns latent codes that are more compressed than a standard VAE while still capturing most non-trivial structure. Finally, we extend our model to a hierarchy of latent variables at different scales. Our model achieves state-of-the-art performance on binarized MNIST, competitive performance on 64 × 64 ImageNet, and high-quality samples on the LSUN bedrooms dataset.
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 combines a hierarchical Variational Autoencoder with PixelCNNs to model the distribution of natural images. They report good (although not state of the art) likelihoods on natural images and briefly start to explore what information is encoded by the latent representations in the hierarchical VAE. I believe that combining the PixelCNN with a VAE, as was already suggested in the PixelCNN paper, is an important and interesting contribution. The encoding of high-, mid- and low-level variations at the different latent stages is interesting but seems not terribly surprising, since the size of the image regions the latent variables model is also at the corresponding scale. Showing that the PixelCNN improves the latent representation of the VAE with regard to some interesting task would be a much stronger result. Also, while the paper claims, that combining the PixelCNN with the VAE reduces the number of computationally expensive autoregressive layers, it remains unclear how much more efficient their whole model is than an PixelCNN with comparable likelihood. In general, I find the clarity of the presentation wanting. For example, I agree with reviewer1 that the exact structure of their model remains unclear from the paper and would be difficult to reproduce.
assistant
The paper combines a hierarchical Variational Autoencoder with PixelCNNs to model the distribution of natural images. They report good (although not state of the art) likelihoods on natural images and briefly start to explore what information is encoded by the latent representations in the hierarchical VAE. I believe that combining the PixelCNN with a VAE, as was already suggested in the PixelCNN paper, is an important and interesting contribution. The encoding of high-, mid- and low-level variations at the different latent stages is interesting but seems not terribly surprising, since the size of the image regions the latent variables model is also at the corresponding scale. Showing that the PixelCNN improves the latent representation of the VAE with regard to some interesting task would be a much stronger result. Also, while the paper claims, that combining the PixelCNN with the VAE reduces the number of computationally expensive autoregressive layers, it remains unclear how much more efficient their whole model is than an PixelCNN with comparable likelihood. In general, I find the clarity of the presentation wanting. For example, I agree with reviewer1 that the exact structure of their model remains unclear from the paper and would be difficult to reproduce.