Anomaly Detection by Deep Direct Density Ratio Estimation
Estimating the ratio of two probability densities without estimating each density separately has been shown to provide useful solutions to various machine learning tasks such as domain adaptation, anomaly detection, feature extraction, and conditional density estimation. However, density ratio estimation in the context of deep learning has not been extensively explored yet. In this paper, we apply a Bregman-divergence minimization method for density ratio estimation to deep neural networks and investigate its properties and practical performance in image anomaly detection. Our numerical experiments on the CIFAR-10, CIFAR-100 and Fashion-MNIST datasets demonstrate that deep direct density ratio estimation greatly improves the anomaly detection ability and reduces the computation time over state-of-the-art methods.
This paper applies a density ratio test based on deep learning for anomaly detection. The general idea behind this paper seems solid and the experimental section seems accurate and the results are good. I had a really hard time understanding exactly what it is that the authors proposed that led to the good results and what the modeling message was. There was a good bit of jargon about various architectures and why the exact model of the authors is a good idea but it was not made clear conceptually what the advantage of their approach was. I think there are good ideas in this paper but they are not distilled well.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Anomaly Detection by Deep Direct Density Ratio Estimation
Estimating the ratio of two probability densities without estimating each density separately has been shown to provide useful solutions to various machine learning tasks such as domain adaptation, anomaly detection, feature extraction, and conditional density estimation. However, density ratio estimation in the context of deep learning has not been extensively explored yet. In this paper, we apply a Bregman-divergence minimization method for density ratio estimation to deep neural networks and investigate its properties and practical performance in image anomaly detection. Our numerical experiments on the CIFAR-10, CIFAR-100 and Fashion-MNIST datasets demonstrate that deep direct density ratio estimation greatly improves the anomaly detection ability and reduces the computation time over state-of-the-art methods.
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 applies a density ratio test based on deep learning for anomaly detection. The general idea behind this paper seems solid and the experimental section seems accurate and the results are good. I had a really hard time understanding exactly what it is that the authors proposed that led to the good results and what the modeling message was. There was a good bit of jargon about various architectures and why the exact model of the authors is a good idea but it was not made clear conceptually what the advantage of their approach was. I think there are good ideas in this paper but they are not distilled well.
assistant
This paper applies a density ratio test based on deep learning for anomaly detection. The general idea behind this paper seems solid and the experimental section seems accurate and the results are good. I had a really hard time understanding exactly what it is that the authors proposed that led to the good results and what the modeling message was. There was a good bit of jargon about various architectures and why the exact model of the authors is a good idea but it was not made clear conceptually what the advantage of their approach was. I think there are good ideas in this paper but they are not distilled well.