HiLLoC: lossless image compression with hierarchical latent variable models
We make the following striking observation: fully convolutional VAE models trained on 32x32 ImageNet can generalize well, not just to 64x64 but also to far larger photographs, with no changes to the model. We use this property, applying fully convolutional models to lossless compression, demonstrating a method to scale the VAE-based *Bits-Back with ANS* algorithm for lossless compression to large color photographs, and achieving state of the art for compression of full size ImageNet images. We release Craystack, an open source library for convenient prototyping of lossless compression using probabilistic models, along with full implementations of all of our compression results.
This paper proposes a method for lossless image compression consisting of a VAE and using a bits-back version of ANS. The results are very impressive on a ImageNet (but maybe not so impressive on the other benchmarks). The authors also discuss how to speed up inference and present some frightening runtime numbers for the serial method, and some better numbers for the vectorized version, though they*re nowhere close to being practical. I think this paper should be accepted. It has a better description of the BB ANS algorithm than I have read before, and it*s a truly interesting direction for the field, despite the lack of immediate applicability. If we are to accept this paper, I suggest the authors put a full description of the neural network used (it*s barely mentioned). I think the authors also need to disclose how long it took to compress an average imagenet image (looking at the runtime numbers for 128x128 pixels is scary, but at least we*d get a better picture on the feasability). Overall, due to the fact that the authors pledge to open source the framework, I think some of the details will be found in the code, once released. I think this is an important step because there are so many details in this paper that one cannot reasonably reproduce the work by simply reading the text of this paper.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
HiLLoC: lossless image compression with hierarchical latent variable models
We make the following striking observation: fully convolutional VAE models trained on 32x32 ImageNet can generalize well, not just to 64x64 but also to far larger photographs, with no changes to the model. We use this property, applying fully convolutional models to lossless compression, demonstrating a method to scale the VAE-based *Bits-Back with ANS* algorithm for lossless compression to large color photographs, and achieving state of the art for compression of full size ImageNet images. We release Craystack, an open source library for convenient prototyping of lossless compression using probabilistic models, along with full implementations of all of our compression results.
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>



This paper proposes a method for lossless image compression consisting of a VAE and using a bits-back version of ANS. The results are very impressive on a ImageNet (but maybe not so impressive on the other benchmarks). The authors also discuss how to speed up inference and present some frightening runtime numbers for the serial method, and some better numbers for the vectorized version, though they*re nowhere close to being practical. I think this paper should be accepted. It has a better description of the BB ANS algorithm than I have read before, and it*s a truly interesting direction for the field, despite the lack of immediate applicability. If we are to accept this paper, I suggest the authors put a full description of the neural network used (it*s barely mentioned). I think the authors also need to disclose how long it took to compress an average imagenet image (looking at the runtime numbers for 128x128 pixels is scary, but at least we*d get a better picture on the feasability). Overall, due to the fact that the authors pledge to open source the framework, I think some of the details will be found in the code, once released. I think this is an important step because there are so many details in this paper that one cannot reasonably reproduce the work by simply reading the text of this paper.
assistant
This paper proposes a method for lossless image compression consisting of a VAE and using a bits-back version of ANS. The results are very impressive on a ImageNet (but maybe not so impressive on the other benchmarks). The authors also discuss how to speed up inference and present some frightening runtime numbers for the serial method, and some better numbers for the vectorized version, though they*re nowhere close to being practical. I think this paper should be accepted. It has a better description of the BB ANS algorithm than I have read before, and it*s a truly interesting direction for the field, despite the lack of immediate applicability. If we are to accept this paper, I suggest the authors put a full description of the neural network used (it*s barely mentioned). I think the authors also need to disclose how long it took to compress an average imagenet image (looking at the runtime numbers for 128x128 pixels is scary, but at least we*d get a better picture on the feasability). Overall, due to the fact that the authors pledge to open source the framework, I think some of the details will be found in the code, once released. I think this is an important step because there are so many details in this paper that one cannot reasonably reproduce the work by simply reading the text of this paper.