UaiNets: From Unsupervised to Active Deep Anomaly Detection
This work presents a method for active anomaly detection which can be built upon existing deep learning solutions for unsupervised anomaly detection. We show that a prior needs to be assumed on what the anomalies are, in order to have performance guarantees in unsupervised anomaly detection. We argue that active anomaly detection has, in practice, the same cost of unsupervised anomaly detection but with the possibility of much better results. To solve this problem, we present a new layer that can be attached to any deep learning model designed for unsupervised anomaly detection to transform it into an active method, presenting results on both synthetic and real anomaly detection datasets.
 This is an interesting paper on a topic with real-world application: anomaly detection. The paper*s organization is, at times quite confusing: - the introduction is unusually short, with a 1st paragraph virtually unreadable due to the abuse of citations. Two additional paragraphs, covering in an intuitive manner both the proposed approach & the main results, would dramatically improve the paper*s readability - section 2.1 starts quite abruptly with he two Lemmas 7 and Theorem 3 (which, in fact, is Theorem 1). This section would probably read a lot better without the two Lemmas, as the authors only refer to the main result in the Theorem. The second, intuitive part of 2.1 is extremely helpful. - it is unclear why the authors have applied the approach in "4.3" only to a single dataset, rather than all the 11 datasets Other comments: - please change the color schemes for Figures 3 & 4, where the red/orange (Fig 3) and various blues (Fig 4) are difficult to distinguish - bottom of page 3: "are rare as expected" --> "are as rare as expected"
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
UaiNets: From Unsupervised to Active Deep Anomaly Detection
This work presents a method for active anomaly detection which can be built upon existing deep learning solutions for unsupervised anomaly detection. We show that a prior needs to be assumed on what the anomalies are, in order to have performance guarantees in unsupervised anomaly detection. We argue that active anomaly detection has, in practice, the same cost of unsupervised anomaly detection but with the possibility of much better results. To solve this problem, we present a new layer that can be attached to any deep learning model designed for unsupervised anomaly detection to transform it into an active method, presenting results on both synthetic and real anomaly detection datasets.
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 is an interesting paper on a topic with real-world application: anomaly detection. The paper*s organization is, at times quite confusing: - the introduction is unusually short, with a 1st paragraph virtually unreadable due to the abuse of citations. Two additional paragraphs, covering in an intuitive manner both the proposed approach & the main results, would dramatically improve the paper*s readability - section 2.1 starts quite abruptly with he two Lemmas 7 and Theorem 3 (which, in fact, is Theorem 1). This section would probably read a lot better without the two Lemmas, as the authors only refer to the main result in the Theorem. The second, intuitive part of 2.1 is extremely helpful. - it is unclear why the authors have applied the approach in "4.3" only to a single dataset, rather than all the 11 datasets Other comments: - please change the color schemes for Figures 3 & 4, where the red/orange (Fig 3) and various blues (Fig 4) are difficult to distinguish - bottom of page 3: "are rare as expected" --> "are as rare as expected"
assistant
 This is an interesting paper on a topic with real-world application: anomaly detection. The paper*s organization is, at times quite confusing: - the introduction is unusually short, with a 1st paragraph virtually unreadable due to the abuse of citations. Two additional paragraphs, covering in an intuitive manner both the proposed approach & the main results, would dramatically improve the paper*s readability - section 2.1 starts quite abruptly with he two Lemmas 7 and Theorem 3 (which, in fact, is Theorem 1). This section would probably read a lot better without the two Lemmas, as the authors only refer to the main result in the Theorem. The second, intuitive part of 2.1 is extremely helpful. - it is unclear why the authors have applied the approach in "4.3" only to a single dataset, rather than all the 11 datasets Other comments: - please change the color schemes for Figures 3 & 4, where the red/orange (Fig 3) and various blues (Fig 4) are difficult to distinguish - bottom of page 3: "are rare as expected" --> "are as rare as expected"