SoftTarget Regularization: An Effective Technique to Reduce Over-Fitting in Neural Networks
Deep neural networks are learning models with a very high capacity and therefore prone to over-fitting. Many regularization techniques such as Dropout, DropConnect, and weight decay all attempt to solve the problem of over-fitting by reducing the capacity of their respective models (Srivastava et al., 2014), (Wan et al., 2013), (Krogh & Hertz, 1992). In this paper we introduce a new form of regularization that guides the learning problem in a way that reduces over-fitting without sacrificing the capacity of the model. The mistakes that models make in early stages of training carry information about the learning problem. By adjusting the labels of the current epoch of training through a weighted average of the real labels, and an exponential average of the past soft-targets we achieved a regularization scheme as powerful as Dropout without necessarily reducing the capacity of the model, and simplified the complexity of the learning problem. SoftTarget regularization proved to be an effective tool in various neural network architectures.
Inspired by the analysis on the effect of the co-label similarity (Hinton et al., 2015), this paper proposes a soft-target regularization that iteratively trains the network using weighted average of the exponential moving average of past labels and hard labels as target argument of loss. They claim that this prevents the disappearing of co-label similarity after early training and yields a competitive regularization to dropout without sacrificing network capacity. In order to make a fair comparison to dropout, the dropout should be tuned carefully. Showing that it performs better than dropout regularization for some particular values of dropout (Table 2) does not demonstrate a convincing advantage. It is possible that dropout performs better after a reasonable tuning with cross-validation. The baseline architectures used in the experiments do not belong the recent state of art methods thus yielding significantly lower accuracy. It seems also that experiment setup does not involve any data augmentation, the results can also change with augmentation. It is not clear why number of epochs are set to a small number like 100 without putting some convergence tests.. Therefore the significance of the method is not convincingly demonstrated in empirical study. Co-label similarities could be calculated using softmax results at final layer rather than using predicted labels. The advantage over dropout is not clear in Figure 4, the dropout is set to 0.2 without any cross-validation. Regularizing by enforcing the training steps to keep co-label similarities is interesting idea but not very novel and the results are not significant. Pros : - provides an investigation of regularization on co-label similarity during training Cons: -The empirical results do not support the intuitive claims regarding proposed procedure Iterative version can be unstable in practice
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
SoftTarget Regularization: An Effective Technique to Reduce Over-Fitting in Neural Networks
Deep neural networks are learning models with a very high capacity and therefore prone to over-fitting. Many regularization techniques such as Dropout, DropConnect, and weight decay all attempt to solve the problem of over-fitting by reducing the capacity of their respective models (Srivastava et al., 2014), (Wan et al., 2013), (Krogh & Hertz, 1992). In this paper we introduce a new form of regularization that guides the learning problem in a way that reduces over-fitting without sacrificing the capacity of the model. The mistakes that models make in early stages of training carry information about the learning problem. By adjusting the labels of the current epoch of training through a weighted average of the real labels, and an exponential average of the past soft-targets we achieved a regularization scheme as powerful as Dropout without necessarily reducing the capacity of the model, and simplified the complexity of the learning problem. SoftTarget regularization proved to be an effective tool in various neural network architectures.
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>



Inspired by the analysis on the effect of the co-label similarity (Hinton et al., 2015), this paper proposes a soft-target regularization that iteratively trains the network using weighted average of the exponential moving average of past labels and hard labels as target argument of loss. They claim that this prevents the disappearing of co-label similarity after early training and yields a competitive regularization to dropout without sacrificing network capacity. In order to make a fair comparison to dropout, the dropout should be tuned carefully. Showing that it performs better than dropout regularization for some particular values of dropout (Table 2) does not demonstrate a convincing advantage. It is possible that dropout performs better after a reasonable tuning with cross-validation. The baseline architectures used in the experiments do not belong the recent state of art methods thus yielding significantly lower accuracy. It seems also that experiment setup does not involve any data augmentation, the results can also change with augmentation. It is not clear why number of epochs are set to a small number like 100 without putting some convergence tests.. Therefore the significance of the method is not convincingly demonstrated in empirical study. Co-label similarities could be calculated using softmax results at final layer rather than using predicted labels. The advantage over dropout is not clear in Figure 4, the dropout is set to 0.2 without any cross-validation. Regularizing by enforcing the training steps to keep co-label similarities is interesting idea but not very novel and the results are not significant. Pros : - provides an investigation of regularization on co-label similarity during training Cons: -The empirical results do not support the intuitive claims regarding proposed procedure Iterative version can be unstable in practice
assistant
Inspired by the analysis on the effect of the co-label similarity (Hinton et al., 2015), this paper proposes a soft-target regularization that iteratively trains the network using weighted average of the exponential moving average of past labels and hard labels as target argument of loss. They claim that this prevents the disappearing of co-label similarity after early training and yields a competitive regularization to dropout without sacrificing network capacity. In order to make a fair comparison to dropout, the dropout should be tuned carefully. Showing that it performs better than dropout regularization for some particular values of dropout (Table 2) does not demonstrate a convincing advantage. It is possible that dropout performs better after a reasonable tuning with cross-validation. The baseline architectures used in the experiments do not belong the recent state of art methods thus yielding significantly lower accuracy. It seems also that experiment setup does not involve any data augmentation, the results can also change with augmentation. It is not clear why number of epochs are set to a small number like 100 without putting some convergence tests.. Therefore the significance of the method is not convincingly demonstrated in empirical study. Co-label similarities could be calculated using softmax results at final layer rather than using predicted labels. The advantage over dropout is not clear in Figure 4, the dropout is set to 0.2 without any cross-validation. Regularizing by enforcing the training steps to keep co-label similarities is interesting idea but not very novel and the results are not significant. Pros : - provides an investigation of regularization on co-label similarity during training Cons: -The empirical results do not support the intuitive claims regarding proposed procedure Iterative version can be unstable in practice