Padam: Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
Adaptive gradient methods, which adopt historical gradient information to automatically adjust the learning rate, despite the nice property of fast convergence, have been observed to generalize worse than stochastic gradient descent (SGD) with momentum in training deep neural networks. This leaves how to close the generalization gap of adaptive gradient methods an open problem. In this work, we show that adaptive gradient methods such as Adam, Amsgrad, are sometimes "over adapted". We design a new algorithm, called Partially adaptive momentum estimation method (Padam), which unifies the Adam/Amsgrad with SGD by introducing a partial adaptive parameter p, to achieve the best from both worlds. Experiments on standard benchmarks show that Padam can maintain fast convergence rate as Adam/Amsgrad while generalizing as well as SGD in training deep neural networks. These results would suggest practitioners pick up adaptive gradient methods once again for faster training of deep neural networks.
 The idea is simple and promising: generalize AMSgrad and momentum by hyperparameterizing the p=1/2 in denominator of ADAM term to be within [0,1/2], with 0 being momentum case. It was good to see the experiments use non-MNIST data (e.g. ImageNet, Cifar) and reasonable CNN models (ResNet, VGG). However, the experimental evaluation is not convincing that this approach will lead to significant improvements in optimizing such modern models in practice. One key concern and flaw in their experimental work, which was not addressed, nor even raised, by the authors as a potential issue, is that their PADAM approach got one extra hyperparameter (p) to tune its performance in their grid search than the competitor optimizers (ADAM, AMSgrad, momentum). So, it is not at all surprising that given it has one extra parameter, that there will be a setting for p that turns out to be a bit better than 0 or 1/2 for any given data/model setup and weight initialization/trajectory examined. So at most this paper represents an existence proof that a value of p other than 0 or 1/2 can be best. It does not provide any guidance on how to find p in a practical way that would lead to wide adoption of PADAM as a replacement for the established competitor optimizers. As Figures 2 and 3 show, momentum ends up converging to as good a solution as PADAM, and so it doesn*t seem to matter in the end that PADAM (or ADAM) might seem to converge a bit faster at the very beginning. This work might have some value in inspiring follow-on work that could try to make this approach practical, such as adapting p somehow during training to lead to truly significant speedups or better generalization. But as experimented and reported so far, this paper does not give readers any reason to switch over to this approach, and so the work is very limited in terms of any significance/impact. Given how simple the modification is, the novelty is also limited, and not sufficient relative to the low significance.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Padam: Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
Adaptive gradient methods, which adopt historical gradient information to automatically adjust the learning rate, despite the nice property of fast convergence, have been observed to generalize worse than stochastic gradient descent (SGD) with momentum in training deep neural networks. This leaves how to close the generalization gap of adaptive gradient methods an open problem. In this work, we show that adaptive gradient methods such as Adam, Amsgrad, are sometimes "over adapted". We design a new algorithm, called Partially adaptive momentum estimation method (Padam), which unifies the Adam/Amsgrad with SGD by introducing a partial adaptive parameter p, to achieve the best from both worlds. Experiments on standard benchmarks show that Padam can maintain fast convergence rate as Adam/Amsgrad while generalizing as well as SGD in training deep neural networks. These results would suggest practitioners pick up adaptive gradient methods once again for faster training of deep neural networks.
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 idea is simple and promising: generalize AMSgrad and momentum by hyperparameterizing the p=1/2 in denominator of ADAM term to be within [0,1/2], with 0 being momentum case. It was good to see the experiments use non-MNIST data (e.g. ImageNet, Cifar) and reasonable CNN models (ResNet, VGG). However, the experimental evaluation is not convincing that this approach will lead to significant improvements in optimizing such modern models in practice. One key concern and flaw in their experimental work, which was not addressed, nor even raised, by the authors as a potential issue, is that their PADAM approach got one extra hyperparameter (p) to tune its performance in their grid search than the competitor optimizers (ADAM, AMSgrad, momentum). So, it is not at all surprising that given it has one extra parameter, that there will be a setting for p that turns out to be a bit better than 0 or 1/2 for any given data/model setup and weight initialization/trajectory examined. So at most this paper represents an existence proof that a value of p other than 0 or 1/2 can be best. It does not provide any guidance on how to find p in a practical way that would lead to wide adoption of PADAM as a replacement for the established competitor optimizers. As Figures 2 and 3 show, momentum ends up converging to as good a solution as PADAM, and so it doesn*t seem to matter in the end that PADAM (or ADAM) might seem to converge a bit faster at the very beginning. This work might have some value in inspiring follow-on work that could try to make this approach practical, such as adapting p somehow during training to lead to truly significant speedups or better generalization. But as experimented and reported so far, this paper does not give readers any reason to switch over to this approach, and so the work is very limited in terms of any significance/impact. Given how simple the modification is, the novelty is also limited, and not sufficient relative to the low significance.
assistant
 The idea is simple and promising: generalize AMSgrad and momentum by hyperparameterizing the p=1/2 in denominator of ADAM term to be within [0,1/2], with 0 being momentum case. It was good to see the experiments use non-MNIST data (e.g. ImageNet, Cifar) and reasonable CNN models (ResNet, VGG). However, the experimental evaluation is not convincing that this approach will lead to significant improvements in optimizing such modern models in practice. One key concern and flaw in their experimental work, which was not addressed, nor even raised, by the authors as a potential issue, is that their PADAM approach got one extra hyperparameter (p) to tune its performance in their grid search than the competitor optimizers (ADAM, AMSgrad, momentum). So, it is not at all surprising that given it has one extra parameter, that there will be a setting for p that turns out to be a bit better than 0 or 1/2 for any given data/model setup and weight initialization/trajectory examined. So at most this paper represents an existence proof that a value of p other than 0 or 1/2 can be best. It does not provide any guidance on how to find p in a practical way that would lead to wide adoption of PADAM as a replacement for the established competitor optimizers. As Figures 2 and 3 show, momentum ends up converging to as good a solution as PADAM, and so it doesn*t seem to matter in the end that PADAM (or ADAM) might seem to converge a bit faster at the very beginning. This work might have some value in inspiring follow-on work that could try to make this approach practical, such as adapting p somehow during training to lead to truly significant speedups or better generalization. But as experimented and reported so far, this paper does not give readers any reason to switch over to this approach, and so the work is very limited in terms of any significance/impact. Given how simple the modification is, the novelty is also limited, and not sufficient relative to the low significance.