Deep Semi-Supervised Anomaly Detection
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets. Typically anomaly detection is treated as an unsupervised learning problem. In practice however, one may have---in addition to a large set of unlabeled samples---access to a small pool of labeled samples, e.g. a subset verified by some domain expert as being normal or anomalous. Semi-supervised approaches to anomaly detection aim to utilize such labeled samples, but most proposed methods are limited to merely including labeled normal samples. Only a few methods take advantage of labeled anomalies, with existing deep approaches being domain-specific. In this work we present Deep SAD, an end-to-end deep methodology for general semi-supervised anomaly detection. Using an information-theoretic perspective on anomaly detection, we derive a loss motivated by the idea that the entropy of the latent distribution for normal data should be lower than the entropy of the anomalous distribution. We demonstrate in extensive experiments on MNIST, Fashion-MNIST, and CIFAR-10, along with other anomaly detection benchmark datasets, that our method is on par or outperforms shallow, hybrid, and deep competitors, yielding appreciable performance improvements even when provided with only little labeled data.
Summary of the work - The work proposes a new method two find anomaly (out of distribution) data when some labeled anomalies are given. - The authors apply information theory-derived loss based on that the normal (in distribution) data usually have lower entropy compared to that of the abnormal data. - The paper conducts extensive experiments on MNIST, Fashion-MNIST, and CIFAR 10, with varying the number of labeled anomlies. I think the paper is well written and the experiment seems to support the authors argument. Unfortunately, this field is not overlapped to my research field, and it is hard for me to judge this paper.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Deep Semi-Supervised Anomaly Detection
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets. Typically anomaly detection is treated as an unsupervised learning problem. In practice however, one may have---in addition to a large set of unlabeled samples---access to a small pool of labeled samples, e.g. a subset verified by some domain expert as being normal or anomalous. Semi-supervised approaches to anomaly detection aim to utilize such labeled samples, but most proposed methods are limited to merely including labeled normal samples. Only a few methods take advantage of labeled anomalies, with existing deep approaches being domain-specific. In this work we present Deep SAD, an end-to-end deep methodology for general semi-supervised anomaly detection. Using an information-theoretic perspective on anomaly detection, we derive a loss motivated by the idea that the entropy of the latent distribution for normal data should be lower than the entropy of the anomalous distribution. We demonstrate in extensive experiments on MNIST, Fashion-MNIST, and CIFAR-10, along with other anomaly detection benchmark datasets, that our method is on par or outperforms shallow, hybrid, and deep competitors, yielding appreciable performance improvements even when provided with only little labeled data.
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>



Summary of the work - The work proposes a new method two find anomaly (out of distribution) data when some labeled anomalies are given. - The authors apply information theory-derived loss based on that the normal (in distribution) data usually have lower entropy compared to that of the abnormal data. - The paper conducts extensive experiments on MNIST, Fashion-MNIST, and CIFAR 10, with varying the number of labeled anomlies. I think the paper is well written and the experiment seems to support the authors argument. Unfortunately, this field is not overlapped to my research field, and it is hard for me to judge this paper.
assistant
Summary of the work - The work proposes a new method two find anomaly (out of distribution) data when some labeled anomalies are given. - The authors apply information theory-derived loss based on that the normal (in distribution) data usually have lower entropy compared to that of the abnormal data. - The paper conducts extensive experiments on MNIST, Fashion-MNIST, and CIFAR 10, with varying the number of labeled anomlies. I think the paper is well written and the experiment seems to support the authors argument. Unfortunately, this field is not overlapped to my research field, and it is hard for me to judge this paper.