Semi-supervised Outlier Detection using Generative And Adversary Framework
In a conventional binary/multi-class classification task, the decision boundary is supported by data from two or more classes. However, in one-class classification task, only data from one class are available. To build an robust outlier detector using only data from a positive class, we propose a corrupted GAN(CorGAN), a deep convolutional Generative Adversary Network requiring no convergence during training. In the adversarial process of training CorGAN, the Generator is supposed to generate outlier samples for negative class, and the Discriminator as an one-class classifier is trained to distinguish data from training datasets (i.e. positive class) and generated data from the Generator (i.e. negative class). To improve the performance of the Discriminator (one-class classifier), we also propose a lot of techniques to improve the performance of the model. The proposed model outperforms the traditional method PCA + PSVM and the solution based on Autoencoder.
 The idea of using GANs for outlier detection is interesting and the problem is relevant. However, I have the following concerns about the quality and the significance: - The proposed formulation in Equation (2) is questionable. The authors say that this is used to generate outliers, and since it will generate inliers when convergence, the authors propose the technique of early stopping in Section 4.1 to avoid convergence. However, then what is learned though the proposed formulation? Since this approach is not straightforward, more theoretical analysis of the proposed method is desirable. - In addition to the above point, I guess the expectation is needed as the original formulation of GAN. Otherwise the proposed formulation does not make sense as it receives only specific data points and how to accumulate objective values across data points is not defined. - In experiments, although the authors say "lots of datasets are used", only two datasets are used, which is not enough to examine the performance of outlier detection methods. Moreover, outliers are artificially generated in these datasets, hence there is no evaluation on pure real-world datasets. To achieve the better quality of the paper, I recommend to add more real-world datasets in experiments. - As discussed in Section 2, there are already many outlier detection methods, such as distance-based outlier detection methods, but they are not compared in experiments. Although the authors argue that distance-based outlier detection methods do not work well for high-dimensional data, this is not always correct. Please see the paper: -- Zimek, A., Schubert, E., Kriegel, H.-P., A survey on unsupervised outlier detection in high-dimensional numerical data, Statistical Analysis and Data Mining (2012) This paper shows that the performance gets even better for higher dimensional data if each feature is relevant. I recommend to add some distance-based outlier detection methods as baselines in experiments. - Since parameter tuning by cross validation cannot be used due to missing information of outliers, it is important to examine the sensitivity of the proposed method with respect to changes in its parameters (a_new, lambda, and others). Otherwise in practice how to set these parameters to get better results is not obvious. * The clarity of this paper is not high as the proposed method is not well explained. In particular, please mathematically formulate each proposed technique in Section 4. * Since the proposed formulation is not convincing due to the above reasons and experimental evaluation is not thorough, the originality is not high. Minor comments: - P.1, L.5 in the third paragraph: architexture -> architecture - What does "Cor" of CorGAN mean? AFTER REVISION Thank you to the authors for their response and revision. Although the paper has been improved, I keep my rating due to the insufficient experimental evaluation.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Semi-supervised Outlier Detection using Generative And Adversary Framework
In a conventional binary/multi-class classification task, the decision boundary is supported by data from two or more classes. However, in one-class classification task, only data from one class are available. To build an robust outlier detector using only data from a positive class, we propose a corrupted GAN(CorGAN), a deep convolutional Generative Adversary Network requiring no convergence during training. In the adversarial process of training CorGAN, the Generator is supposed to generate outlier samples for negative class, and the Discriminator as an one-class classifier is trained to distinguish data from training datasets (i.e. positive class) and generated data from the Generator (i.e. negative class). To improve the performance of the Discriminator (one-class classifier), we also propose a lot of techniques to improve the performance of the model. The proposed model outperforms the traditional method PCA + PSVM and the solution based on Autoencoder.
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 idea of using GANs for outlier detection is interesting and the problem is relevant. However, I have the following concerns about the quality and the significance: - The proposed formulation in Equation (2) is questionable. The authors say that this is used to generate outliers, and since it will generate inliers when convergence, the authors propose the technique of early stopping in Section 4.1 to avoid convergence. However, then what is learned though the proposed formulation? Since this approach is not straightforward, more theoretical analysis of the proposed method is desirable. - In addition to the above point, I guess the expectation is needed as the original formulation of GAN. Otherwise the proposed formulation does not make sense as it receives only specific data points and how to accumulate objective values across data points is not defined. - In experiments, although the authors say "lots of datasets are used", only two datasets are used, which is not enough to examine the performance of outlier detection methods. Moreover, outliers are artificially generated in these datasets, hence there is no evaluation on pure real-world datasets. To achieve the better quality of the paper, I recommend to add more real-world datasets in experiments. - As discussed in Section 2, there are already many outlier detection methods, such as distance-based outlier detection methods, but they are not compared in experiments. Although the authors argue that distance-based outlier detection methods do not work well for high-dimensional data, this is not always correct. Please see the paper: -- Zimek, A., Schubert, E., Kriegel, H.-P., A survey on unsupervised outlier detection in high-dimensional numerical data, Statistical Analysis and Data Mining (2012) This paper shows that the performance gets even better for higher dimensional data if each feature is relevant. I recommend to add some distance-based outlier detection methods as baselines in experiments. - Since parameter tuning by cross validation cannot be used due to missing information of outliers, it is important to examine the sensitivity of the proposed method with respect to changes in its parameters (a_new, lambda, and others). Otherwise in practice how to set these parameters to get better results is not obvious. * The clarity of this paper is not high as the proposed method is not well explained. In particular, please mathematically formulate each proposed technique in Section 4. * Since the proposed formulation is not convincing due to the above reasons and experimental evaluation is not thorough, the originality is not high. Minor comments: - P.1, L.5 in the third paragraph: architexture -> architecture - What does "Cor" of CorGAN mean? AFTER REVISION Thank you to the authors for their response and revision. Although the paper has been improved, I keep my rating due to the insufficient experimental evaluation.
assistant
 The idea of using GANs for outlier detection is interesting and the problem is relevant. However, I have the following concerns about the quality and the significance: - The proposed formulation in Equation (2) is questionable. The authors say that this is used to generate outliers, and since it will generate inliers when convergence, the authors propose the technique of early stopping in Section 4.1 to avoid convergence. However, then what is learned though the proposed formulation? Since this approach is not straightforward, more theoretical analysis of the proposed method is desirable. - In addition to the above point, I guess the expectation is needed as the original formulation of GAN. Otherwise the proposed formulation does not make sense as it receives only specific data points and how to accumulate objective values across data points is not defined. - In experiments, although the authors say "lots of datasets are used", only two datasets are used, which is not enough to examine the performance of outlier detection methods. Moreover, outliers are artificially generated in these datasets, hence there is no evaluation on pure real-world datasets. To achieve the better quality of the paper, I recommend to add more real-world datasets in experiments. - As discussed in Section 2, there are already many outlier detection methods, such as distance-based outlier detection methods, but they are not compared in experiments. Although the authors argue that distance-based outlier detection methods do not work well for high-dimensional data, this is not always correct. Please see the paper: -- Zimek, A., Schubert, E., Kriegel, H.-P., A survey on unsupervised outlier detection in high-dimensional numerical data, Statistical Analysis and Data Mining (2012) This paper shows that the performance gets even better for higher dimensional data if each feature is relevant. I recommend to add some distance-based outlier detection methods as baselines in experiments. - Since parameter tuning by cross validation cannot be used due to missing information of outliers, it is important to examine the sensitivity of the proposed method with respect to changes in its parameters (a_new, lambda, and others). Otherwise in practice how to set these parameters to get better results is not obvious. * The clarity of this paper is not high as the proposed method is not well explained. In particular, please mathematically formulate each proposed technique in Section 4. * Since the proposed formulation is not convincing due to the above reasons and experimental evaluation is not thorough, the originality is not high. Minor comments: - P.1, L.5 in the third paragraph: architexture -> architecture - What does "Cor" of CorGAN mean? AFTER REVISION Thank you to the authors for their response and revision. Although the paper has been improved, I keep my rating due to the insufficient experimental evaluation.