Smart Ternary Quantization
Neural network models are resource hungry. Low bit quantization such as binary and ternary quantization is a common approach to alleviate this resource requirements. Ternary quantization provides a more flexible model and often beats binary quantization in terms of accuracy, but doubles memory and increases computation cost. Mixed quantization depth models, on another hand, allows a trade-off between accuracy and memory footprint. In such models, quantization depth is often chosen manually (which is a tiring task), or is tuned using a separate optimization routine (which requires training a quantized network multiple times). Here, we propose Smart Ternary Quantization (STQ) in which we modify the quantization depth directly through an adaptive regularization function, so that we train a model only once. This method jumps between binary and ternary quantization while training. We show its application on image classification.
The Paper talks about the Smart Ternary Quantization method that improves the quantization over binary and ternary quantizations by specifying an adaptive quantization. The proposed regularization function is covered in detail and the results are evaluated on MNIST and CIFAR10 datasets The authors can improve the submission by 1. evaluating more modern networks with bigger datasets, as opposed to the ones demonstrated. 2. describing gamma (eq 10) , it wasn*t clear why that parameter was introduced (in addition to the beta) and it*s significance, what was more confusing is coverage for this instead of beta in the experimental setup 3. why the LR needed to be modified for the described method
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Smart Ternary Quantization
Neural network models are resource hungry. Low bit quantization such as binary and ternary quantization is a common approach to alleviate this resource requirements. Ternary quantization provides a more flexible model and often beats binary quantization in terms of accuracy, but doubles memory and increases computation cost. Mixed quantization depth models, on another hand, allows a trade-off between accuracy and memory footprint. In such models, quantization depth is often chosen manually (which is a tiring task), or is tuned using a separate optimization routine (which requires training a quantized network multiple times). Here, we propose Smart Ternary Quantization (STQ) in which we modify the quantization depth directly through an adaptive regularization function, so that we train a model only once. This method jumps between binary and ternary quantization while training. We show its application on image classification.
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 talks about the Smart Ternary Quantization method that improves the quantization over binary and ternary quantizations by specifying an adaptive quantization. The proposed regularization function is covered in detail and the results are evaluated on MNIST and CIFAR10 datasets The authors can improve the submission by 1. evaluating more modern networks with bigger datasets, as opposed to the ones demonstrated. 2. describing gamma (eq 10) , it wasn*t clear why that parameter was introduced (in addition to the beta) and it*s significance, what was more confusing is coverage for this instead of beta in the experimental setup 3. why the LR needed to be modified for the described method
assistant
The Paper talks about the Smart Ternary Quantization method that improves the quantization over binary and ternary quantizations by specifying an adaptive quantization. The proposed regularization function is covered in detail and the results are evaluated on MNIST and CIFAR10 datasets The authors can improve the submission by 1. evaluating more modern networks with bigger datasets, as opposed to the ones demonstrated. 2. describing gamma (eq 10) , it wasn*t clear why that parameter was introduced (in addition to the beta) and it*s significance, what was more confusing is coverage for this instead of beta in the experimental setup 3. why the LR needed to be modified for the described method