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.
 This paper proposes a small modification to current adaptive gradient methods by introducing a partial adaptive parameter, showing improved generalization performance in several image classification benchmarks. Pros: - The modification is simple and easy to implement. - The proposed method shows improved performance across different datasets, including ImageNet. Cons: - Missing an important baseline - AdamW (https://arxiv.org/pdf/1711.05101.pdf) which shows that Adam can generalize as well as SGD and retain faster training. Basically, the poor generalization performance of Adam is due to the incorrect implementation of weight decay. - The experimental results for Adam are not convincing. It*s well-known that Adam is good at training but might perform badly on the test data. However, Adam performs much worse than SGD in terms of training loss in all plots, which is contrary to my expectation. I doubt that Adam is not tuned well. One possible explanation is that the training budget is not enough, first-order methods typically require 200 epochs to converge. So I suggest the authors training the networks longer (make sure the training loss levels off before the first drop of learning rate.). - Mixing the concept of generalization and test performance. Note that generalization performance typically measures the gap between training and test error. To make the comparison fair, please make sure the training error is zero (I expect both training error and training loss should be close to 0 on CIFAR). - In terms of optimization (convergence) performance, I cannot think of any reason that the proposed method would outperform Adam (or Amsgrad). The convergence analysis doesn*t say anything meaningful.
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>



 This paper proposes a small modification to current adaptive gradient methods by introducing a partial adaptive parameter, showing improved generalization performance in several image classification benchmarks. Pros: - The modification is simple and easy to implement. - The proposed method shows improved performance across different datasets, including ImageNet. Cons: - Missing an important baseline - AdamW (https://arxiv.org/pdf/1711.05101.pdf) which shows that Adam can generalize as well as SGD and retain faster training. Basically, the poor generalization performance of Adam is due to the incorrect implementation of weight decay. - The experimental results for Adam are not convincing. It*s well-known that Adam is good at training but might perform badly on the test data. However, Adam performs much worse than SGD in terms of training loss in all plots, which is contrary to my expectation. I doubt that Adam is not tuned well. One possible explanation is that the training budget is not enough, first-order methods typically require 200 epochs to converge. So I suggest the authors training the networks longer (make sure the training loss levels off before the first drop of learning rate.). - Mixing the concept of generalization and test performance. Note that generalization performance typically measures the gap between training and test error. To make the comparison fair, please make sure the training error is zero (I expect both training error and training loss should be close to 0 on CIFAR). - In terms of optimization (convergence) performance, I cannot think of any reason that the proposed method would outperform Adam (or Amsgrad). The convergence analysis doesn*t say anything meaningful.
assistant
 This paper proposes a small modification to current adaptive gradient methods by introducing a partial adaptive parameter, showing improved generalization performance in several image classification benchmarks. Pros: - The modification is simple and easy to implement. - The proposed method shows improved performance across different datasets, including ImageNet. Cons: - Missing an important baseline - AdamW (https://arxiv.org/pdf/1711.05101.pdf) which shows that Adam can generalize as well as SGD and retain faster training. Basically, the poor generalization performance of Adam is due to the incorrect implementation of weight decay. - The experimental results for Adam are not convincing. It*s well-known that Adam is good at training but might perform badly on the test data. However, Adam performs much worse than SGD in terms of training loss in all plots, which is contrary to my expectation. I doubt that Adam is not tuned well. One possible explanation is that the training budget is not enough, first-order methods typically require 200 epochs to converge. So I suggest the authors training the networks longer (make sure the training loss levels off before the first drop of learning rate.). - Mixing the concept of generalization and test performance. Note that generalization performance typically measures the gap between training and test error. To make the comparison fair, please make sure the training error is zero (I expect both training error and training loss should be close to 0 on CIFAR). - In terms of optimization (convergence) performance, I cannot think of any reason that the proposed method would outperform Adam (or Amsgrad). The convergence analysis doesn*t say anything meaningful.