Adjusting for Dropout Variance in Batch Normalization and Weight Initialization
We show how to adjust for the variance introduced by dropout with corrections to weight initialization and Batch Normalization, yielding higher accuracy. Though dropout can preserve the expected input to a neuron between train and test, the variance of the input differs. We thus propose a new weight initialization by correcting for the influence of dropout rates and an arbitrary nonlinearity*s influence on variance through simple corrective scalars. Since Batch Normalization trained with dropout estimates the variance of a layer*s incoming distribution with some inputs dropped, the variance also differs between train and test. After training a network with Batch Normalization and dropout, we simply update Batch Normalization*s variance moving averages with dropout off and obtain state of the art on CIFAR-10 and CIFAR-100 without data augmentation.
The paper presents an approach for compensating the input/activation variance introduced by dropout in a network. Additionally, a practical inference trick of re-estimating the batch normalization parameters with dropout turned off before testing. The authors very well show how dropout influences the input/activation variance and then scale the initial weights accordingly to achieve unit variance which helps in avoiding activation outputs exploding or vanishing. It is shown that the presented approach serves as a good initialization technique for deep networks and results in performances op par or slightly better than the existing approaches. The limited experimental validation and only small difference in accuracies compared to existing methods makes it difficult to judge the effectiveness of presented approach. Perhaps observing the statistics of output activations and gradients over training epochs in multiple experiments can better support the argument of stability of the network using proposed approach. Authors might consider adding some validation for considering the backpropagation variance. On multiple occasions comparison is drawn against batch normalization which I believe does much more than a weight initialization technique. The presented approach is a good initialization technique just not sure if its better than existing ones.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Adjusting for Dropout Variance in Batch Normalization and Weight Initialization
We show how to adjust for the variance introduced by dropout with corrections to weight initialization and Batch Normalization, yielding higher accuracy. Though dropout can preserve the expected input to a neuron between train and test, the variance of the input differs. We thus propose a new weight initialization by correcting for the influence of dropout rates and an arbitrary nonlinearity*s influence on variance through simple corrective scalars. Since Batch Normalization trained with dropout estimates the variance of a layer*s incoming distribution with some inputs dropped, the variance also differs between train and test. After training a network with Batch Normalization and dropout, we simply update Batch Normalization*s variance moving averages with dropout off and obtain state of the art on CIFAR-10 and CIFAR-100 without data augmentation.
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 presents an approach for compensating the input/activation variance introduced by dropout in a network. Additionally, a practical inference trick of re-estimating the batch normalization parameters with dropout turned off before testing. The authors very well show how dropout influences the input/activation variance and then scale the initial weights accordingly to achieve unit variance which helps in avoiding activation outputs exploding or vanishing. It is shown that the presented approach serves as a good initialization technique for deep networks and results in performances op par or slightly better than the existing approaches. The limited experimental validation and only small difference in accuracies compared to existing methods makes it difficult to judge the effectiveness of presented approach. Perhaps observing the statistics of output activations and gradients over training epochs in multiple experiments can better support the argument of stability of the network using proposed approach. Authors might consider adding some validation for considering the backpropagation variance. On multiple occasions comparison is drawn against batch normalization which I believe does much more than a weight initialization technique. The presented approach is a good initialization technique just not sure if its better than existing ones.
assistant
The paper presents an approach for compensating the input/activation variance introduced by dropout in a network. Additionally, a practical inference trick of re-estimating the batch normalization parameters with dropout turned off before testing. The authors very well show how dropout influences the input/activation variance and then scale the initial weights accordingly to achieve unit variance which helps in avoiding activation outputs exploding or vanishing. It is shown that the presented approach serves as a good initialization technique for deep networks and results in performances op par or slightly better than the existing approaches. The limited experimental validation and only small difference in accuracies compared to existing methods makes it difficult to judge the effectiveness of presented approach. Perhaps observing the statistics of output activations and gradients over training epochs in multiple experiments can better support the argument of stability of the network using proposed approach. Authors might consider adding some validation for considering the backpropagation variance. On multiple occasions comparison is drawn against batch normalization which I believe does much more than a weight initialization technique. The presented approach is a good initialization technique just not sure if its better than existing ones.