Fixing Weight Decay Regularization in Adam
We note that common implementations of adaptive gradient algorithms, such as Adam, limit the potential benefit of weight decay regularization, because the weights do not decay multiplicatively (as would be expected for standard weight decay) but by an additive constant factor. We propose a simple way to resolve this issue by decoupling weight decay and the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam, and (ii) substantially improves Adam*s generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). We also demonstrate that longer optimization runs require smaller weight decay values for optimal results and introduce a normalized variant of weight decay to reduce this dependence. Finally, we propose a version of Adam with warm restarts (AdamWR) that has strong anytime performance while achieving state-of-the-art results on CIFAR-10 and ImageNet32x32. Our source code will become available after the review process.
 This paper investigates weight decay issues lied in the SGD variants, especially Adam. Current implementations of adaptive gradient algorithms implicitly contain a crucial flaw, by which weight decay in these methods does not correspond to L2 regularization. To fix this issue, this paper proposes the decoupling method between weight decay and the gradient-based update. Overall, this paper is well-written and contain sufficient references to note the overview of recent adaptive gradient-based methods for DNN. In addition, this paper investigates the crucial issue in the recent adaptive gradient methods and find the problem in weight decay. This is an interesting finding. And the proposed method to fix this issue is simple and reasonable. Their experimental results to validate the effectiveness of their proposed method are well-organized. In particular, the investigation on hyperparameter spaces shows the strong advantage of the proposed methods.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Fixing Weight Decay Regularization in Adam
We note that common implementations of adaptive gradient algorithms, such as Adam, limit the potential benefit of weight decay regularization, because the weights do not decay multiplicatively (as would be expected for standard weight decay) but by an additive constant factor. We propose a simple way to resolve this issue by decoupling weight decay and the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam, and (ii) substantially improves Adam*s generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). We also demonstrate that longer optimization runs require smaller weight decay values for optimal results and introduce a normalized variant of weight decay to reduce this dependence. Finally, we propose a version of Adam with warm restarts (AdamWR) that has strong anytime performance while achieving state-of-the-art results on CIFAR-10 and ImageNet32x32. Our source code will become available after the review process.
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 investigates weight decay issues lied in the SGD variants, especially Adam. Current implementations of adaptive gradient algorithms implicitly contain a crucial flaw, by which weight decay in these methods does not correspond to L2 regularization. To fix this issue, this paper proposes the decoupling method between weight decay and the gradient-based update. Overall, this paper is well-written and contain sufficient references to note the overview of recent adaptive gradient-based methods for DNN. In addition, this paper investigates the crucial issue in the recent adaptive gradient methods and find the problem in weight decay. This is an interesting finding. And the proposed method to fix this issue is simple and reasonable. Their experimental results to validate the effectiveness of their proposed method are well-organized. In particular, the investigation on hyperparameter spaces shows the strong advantage of the proposed methods.
assistant
 This paper investigates weight decay issues lied in the SGD variants, especially Adam. Current implementations of adaptive gradient algorithms implicitly contain a crucial flaw, by which weight decay in these methods does not correspond to L2 regularization. To fix this issue, this paper proposes the decoupling method between weight decay and the gradient-based update. Overall, this paper is well-written and contain sufficient references to note the overview of recent adaptive gradient-based methods for DNN. In addition, this paper investigates the crucial issue in the recent adaptive gradient methods and find the problem in weight decay. This is an interesting finding. And the proposed method to fix this issue is simple and reasonable. Their experimental results to validate the effectiveness of their proposed method are well-organized. In particular, the investigation on hyperparameter spaces shows the strong advantage of the proposed methods.