Overfitting Detection of Deep Neural Networks without a Hold Out Set
Overfitting is an ubiquitous problem in neural network training and usually mitigated using a holdout data set. Here we challenge this rationale and investigate criteria for overfitting without using a holdout data set. Specifically, we train a model for a fixed number of epochs multiple times with varying fractions of randomized labels and for a range of regularization strengths. A properly trained model should not be able to attain an accuracy greater than the fraction of properly labeled data points. Otherwise the model overfits. We introduce two criteria for detecting overfitting and one to detect underfitting. We analyze early stopping, the regularization factor, and network depth. In safety critical applications we are interested in models and parameter settings which perform well and are not likely to overfit. The methods of this paper allow characterizing and identifying such models.
 This paper proposed criteria to measure the capacity of a neural network by injecting perturbation (randomized training data). The paper attempted to show that -regularization of the kernel weights is a good measure to control the capacity of a network which contradicts the previous finding by Zhang et al (2017) on regularization which claimed that regularization is neither necessary nor by itself sufficient for controlling generalization error. The proposed method does not require a held out data to check overfitting, which is an interesting direction to explore. The theoretical analysis is seeming to be correct, however, I don’t have strong expertise in theory, therefore, can not assure the correctness. The experiments, however, are limited. The experiment was done on cifar-10 and the analysis is based on the early stopping, regularization factor and network depth. There is only one dataset that was used for the experiments, more dataset should be explored for robust evaluation. The assumptions should be clarified and write clearly. For example, “Thus we also expect that accuracy drops if the regularization of the model is increased.”, which accuracy (training?) and what exactly means by increased regularization (value of )?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Overfitting Detection of Deep Neural Networks without a Hold Out Set
Overfitting is an ubiquitous problem in neural network training and usually mitigated using a holdout data set. Here we challenge this rationale and investigate criteria for overfitting without using a holdout data set. Specifically, we train a model for a fixed number of epochs multiple times with varying fractions of randomized labels and for a range of regularization strengths. A properly trained model should not be able to attain an accuracy greater than the fraction of properly labeled data points. Otherwise the model overfits. We introduce two criteria for detecting overfitting and one to detect underfitting. We analyze early stopping, the regularization factor, and network depth. In safety critical applications we are interested in models and parameter settings which perform well and are not likely to overfit. The methods of this paper allow characterizing and identifying such models.
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 proposed criteria to measure the capacity of a neural network by injecting perturbation (randomized training data). The paper attempted to show that -regularization of the kernel weights is a good measure to control the capacity of a network which contradicts the previous finding by Zhang et al (2017) on regularization which claimed that regularization is neither necessary nor by itself sufficient for controlling generalization error. The proposed method does not require a held out data to check overfitting, which is an interesting direction to explore. The theoretical analysis is seeming to be correct, however, I don’t have strong expertise in theory, therefore, can not assure the correctness. The experiments, however, are limited. The experiment was done on cifar-10 and the analysis is based on the early stopping, regularization factor and network depth. There is only one dataset that was used for the experiments, more dataset should be explored for robust evaluation. The assumptions should be clarified and write clearly. For example, “Thus we also expect that accuracy drops if the regularization of the model is increased.”, which accuracy (training?) and what exactly means by increased regularization (value of )?
assistant
 This paper proposed criteria to measure the capacity of a neural network by injecting perturbation (randomized training data). The paper attempted to show that -regularization of the kernel weights is a good measure to control the capacity of a network which contradicts the previous finding by Zhang et al (2017) on regularization which claimed that regularization is neither necessary nor by itself sufficient for controlling generalization error. The proposed method does not require a held out data to check overfitting, which is an interesting direction to explore. The theoretical analysis is seeming to be correct, however, I don’t have strong expertise in theory, therefore, can not assure the correctness. The experiments, however, are limited. The experiment was done on cifar-10 and the analysis is based on the early stopping, regularization factor and network depth. There is only one dataset that was used for the experiments, more dataset should be explored for robust evaluation. The assumptions should be clarified and write clearly. For example, “Thus we also expect that accuracy drops if the regularization of the model is increased.”, which accuracy (training?) and what exactly means by increased regularization (value of )?