Soft Weight-Sharing for Neural Network Compression
The success of deep learning in numerous application domains created the desire to run and train them on mobile devices. This however, conflicts with their computationally, memory and energy intense nature, leading to a growing interest in compression. Recent work by Han et al. (2016) propose a pipeline that involves retraining, pruning and quantization of neural network weights, obtaining state-of-the-art compression rates. In this paper, we show that competitive compression rates can be achieved by using a version of *soft weight-sharing* (Nowlan & Hinton, 1991). Our method achieves both quantization and pruning in one simple (re-)training procedure. This point of view also exposes the relation between compression and the minimum description length (MDL) principle.
This paper revives a classic idea involving regularization for purposes of compression for modern CNN models on resource constrained devices. Model compression is hot and we*re in the midst of lots of people rediscovering old ideas in this area so it is nice to have a paper that explicitly draws upon classic approaches from the early 90s to obtain competitive results on standard benchmarks. There*s not too much to say here: this study is an instance of a simple idea applied effectively to an important problem, written up in an illuminating manner with appropriate references to classic approaches. The addition of the filter visualizations enhances the contribution.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Soft Weight-Sharing for Neural Network Compression
The success of deep learning in numerous application domains created the desire to run and train them on mobile devices. This however, conflicts with their computationally, memory and energy intense nature, leading to a growing interest in compression. Recent work by Han et al. (2016) propose a pipeline that involves retraining, pruning and quantization of neural network weights, obtaining state-of-the-art compression rates. In this paper, we show that competitive compression rates can be achieved by using a version of *soft weight-sharing* (Nowlan & Hinton, 1991). Our method achieves both quantization and pruning in one simple (re-)training procedure. This point of view also exposes the relation between compression and the minimum description length (MDL) principle.
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 revives a classic idea involving regularization for purposes of compression for modern CNN models on resource constrained devices. Model compression is hot and we*re in the midst of lots of people rediscovering old ideas in this area so it is nice to have a paper that explicitly draws upon classic approaches from the early 90s to obtain competitive results on standard benchmarks. There*s not too much to say here: this study is an instance of a simple idea applied effectively to an important problem, written up in an illuminating manner with appropriate references to classic approaches. The addition of the filter visualizations enhances the contribution.
assistant
This paper revives a classic idea involving regularization for purposes of compression for modern CNN models on resource constrained devices. Model compression is hot and we*re in the midst of lots of people rediscovering old ideas in this area so it is nice to have a paper that explicitly draws upon classic approaches from the early 90s to obtain competitive results on standard benchmarks. There*s not too much to say here: this study is an instance of a simple idea applied effectively to an important problem, written up in an illuminating manner with appropriate references to classic approaches. The addition of the filter visualizations enhances the contribution.