Random Partition Relaxation for Training Binary and Ternary Weight Neural Network
We present Random Partition Relaxation (RPR), a method for strong quantization of the parameters of convolutional neural networks to binary (+1/-1) and ternary (+1/0/-1) values. Starting from a pretrained model, we first quantize the weights and then relax random partitions of them to their continuous values for retraining before quantizing them again and switching to another weight partition for further adaptation. We empirically evaluate the performance of RPR with ResNet-18, ResNet-50 and GoogLeNet on the ImageNet classification task for binary and ternary weight networks. We show accuracies beyond the state-of-the-art for binary- and ternary-weight GoogLeNet and competitive performance for ResNet-18 and ResNet-50 using a SGD-based training method that can easily be integrated into existing frameworks.
Authors propose RPR - a way to randomly partition and quantize weights and train the remaining parameters followed by relaxation in alternate cycles to train quantized models. The authors show the results on latest network architectures on ImageNet dataset - ResNet18, ResNet-50 and GoogLeNet and the results either compare to other quantization methodologies and in the case of GoogLeNet they surpass the state of the art ADMM method by 1.7% top-1 accuracy The current experimental section does not have any runtime or model size numbers, it would be great if authors could talk about that and maybe add that information to get a more apples to apples comparison on not just accuracy but other factors. Also, while Figure1 is a useful visualization of how weights change in the RPR method what is missing in the paper is a more technical/mathematical explanation of the procedure as well as detailed discussion on the hyper-parameters and how things change if we choose a different ratio for the partitioning. As it is, the paper currently is easy to read, seems like a good idea and has good results but I feel it is important to add more detail in both methodology and in experimental results especially have more ablations and understand what is truly happening to the model.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Random Partition Relaxation for Training Binary and Ternary Weight Neural Network
We present Random Partition Relaxation (RPR), a method for strong quantization of the parameters of convolutional neural networks to binary (+1/-1) and ternary (+1/0/-1) values. Starting from a pretrained model, we first quantize the weights and then relax random partitions of them to their continuous values for retraining before quantizing them again and switching to another weight partition for further adaptation. We empirically evaluate the performance of RPR with ResNet-18, ResNet-50 and GoogLeNet on the ImageNet classification task for binary and ternary weight networks. We show accuracies beyond the state-of-the-art for binary- and ternary-weight GoogLeNet and competitive performance for ResNet-18 and ResNet-50 using a SGD-based training method that can easily be integrated into existing frameworks.
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>



Authors propose RPR - a way to randomly partition and quantize weights and train the remaining parameters followed by relaxation in alternate cycles to train quantized models. The authors show the results on latest network architectures on ImageNet dataset - ResNet18, ResNet-50 and GoogLeNet and the results either compare to other quantization methodologies and in the case of GoogLeNet they surpass the state of the art ADMM method by 1.7% top-1 accuracy The current experimental section does not have any runtime or model size numbers, it would be great if authors could talk about that and maybe add that information to get a more apples to apples comparison on not just accuracy but other factors. Also, while Figure1 is a useful visualization of how weights change in the RPR method what is missing in the paper is a more technical/mathematical explanation of the procedure as well as detailed discussion on the hyper-parameters and how things change if we choose a different ratio for the partitioning. As it is, the paper currently is easy to read, seems like a good idea and has good results but I feel it is important to add more detail in both methodology and in experimental results especially have more ablations and understand what is truly happening to the model.
assistant
Authors propose RPR - a way to randomly partition and quantize weights and train the remaining parameters followed by relaxation in alternate cycles to train quantized models. The authors show the results on latest network architectures on ImageNet dataset - ResNet18, ResNet-50 and GoogLeNet and the results either compare to other quantization methodologies and in the case of GoogLeNet they surpass the state of the art ADMM method by 1.7% top-1 accuracy The current experimental section does not have any runtime or model size numbers, it would be great if authors could talk about that and maybe add that information to get a more apples to apples comparison on not just accuracy but other factors. Also, while Figure1 is a useful visualization of how weights change in the RPR method what is missing in the paper is a more technical/mathematical explanation of the procedure as well as detailed discussion on the hyper-parameters and how things change if we choose a different ratio for the partitioning. As it is, the paper currently is easy to read, seems like a good idea and has good results but I feel it is important to add more detail in both methodology and in experimental results especially have more ablations and understand what is truly happening to the model.