Increasing batch size through instance repetition improves generalization
Large-batch SGD is important for scaling training of deep neural networks. However, without fine-tuning hyperparameter schedules, the generalization of the model may be hampered. We propose to use batch augmentation: replicating instances of samples within the same batch with different data augmentations. Batch augmentation acts as a regularizer and an accelerator, increasing both generalization and performance scaling for a fixed budget of optimization steps. We analyze the effect of batch augmentation on gradient variance and show that it empirically improves convergence for a wide variety of networks and datasets. Our results show that batch augmentation reduces the number of necessary SGD updates to achieve the same accuracy as the state-of-the-art. Overall, this simple yet effective method enables faster training and better generalization by allowing more computational resources to be used concurrently.
The paper proposes a very simple strategy to reduce the variance of a batch of training data. It applies data augmentation operations to expand a training example, so that the mini-batch solely consists of the variants of this example. Since the correlations among examples within the batch is stronger than those in a batch with different examples, the gradient has less variance. While it can reduce the gradient variance during training, which leads to faster convergence, it also has better generalization performance. This is confirmed by empirical studies on difference datasets with a variety of neural networks, such lstm, transformer and convnet. Overall, this seems to be a simple yet effective method to improve the convergence and generalization at the same time, which is orthogonal to existing approaches. It might be better to try this method and improve the state-of-the-art on at least one dateset.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Increasing batch size through instance repetition improves generalization
Large-batch SGD is important for scaling training of deep neural networks. However, without fine-tuning hyperparameter schedules, the generalization of the model may be hampered. We propose to use batch augmentation: replicating instances of samples within the same batch with different data augmentations. Batch augmentation acts as a regularizer and an accelerator, increasing both generalization and performance scaling for a fixed budget of optimization steps. We analyze the effect of batch augmentation on gradient variance and show that it empirically improves convergence for a wide variety of networks and datasets. Our results show that batch augmentation reduces the number of necessary SGD updates to achieve the same accuracy as the state-of-the-art. Overall, this simple yet effective method enables faster training and better generalization by allowing more computational resources to be used concurrently.
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 proposes a very simple strategy to reduce the variance of a batch of training data. It applies data augmentation operations to expand a training example, so that the mini-batch solely consists of the variants of this example. Since the correlations among examples within the batch is stronger than those in a batch with different examples, the gradient has less variance. While it can reduce the gradient variance during training, which leads to faster convergence, it also has better generalization performance. This is confirmed by empirical studies on difference datasets with a variety of neural networks, such lstm, transformer and convnet. Overall, this seems to be a simple yet effective method to improve the convergence and generalization at the same time, which is orthogonal to existing approaches. It might be better to try this method and improve the state-of-the-art on at least one dateset.
assistant
The paper proposes a very simple strategy to reduce the variance of a batch of training data. It applies data augmentation operations to expand a training example, so that the mini-batch solely consists of the variants of this example. Since the correlations among examples within the batch is stronger than those in a batch with different examples, the gradient has less variance. While it can reduce the gradient variance during training, which leads to faster convergence, it also has better generalization performance. This is confirmed by empirical studies on difference datasets with a variety of neural networks, such lstm, transformer and convnet. Overall, this seems to be a simple yet effective method to improve the convergence and generalization at the same time, which is orthogonal to existing approaches. It might be better to try this method and improve the state-of-the-art on at least one dateset.