Anomalous Pattern Detection in Activations and Reconstruction Error of Autoencoders
In real-world machine learning applications, large outliers and pervasive noise are commonplace, and access to clean training data as required by standard deep autoencoders is unlikely. Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical image analysis. Autoencoder neural networks learn to reconstruct normal images, and hence can classify those images as anomalous if the reconstruction error exceeds some threshold. In this paper, we proposed an unsupervised method based on subset scanning over autoencoder activations. The contributions of our work are threefold. First, we propose a novel method combining detection with reconstruction error and subset scanning scores to improve the anomaly score of current autoencoders without requiring any retraining. Second, we provide the ability to inspect and visualize the set of anomalous nodes in the reconstruction error space that make a sample noised. Third, we show that subset scanning can be used for anomaly detection in the inner layers of the autoencoder. We provide detection power results for several untargeted adversarial noise models under standard datasets.
I read the paper and found the approach of treating the adversarial detection problem as anomalous pattern detection quite interesting. The authors do a good job of explaining the relative work, the methods their work builds upon. The analysis provided on various layers (Table 1) is quite insightful. However, I am not convinced by the results and found them hard to compare. The datasets used are also MNIST or Fashion MNIST and doesn*t really capture the real case / challenging scenario for the anomaly detection.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Anomalous Pattern Detection in Activations and Reconstruction Error of Autoencoders
In real-world machine learning applications, large outliers and pervasive noise are commonplace, and access to clean training data as required by standard deep autoencoders is unlikely. Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical image analysis. Autoencoder neural networks learn to reconstruct normal images, and hence can classify those images as anomalous if the reconstruction error exceeds some threshold. In this paper, we proposed an unsupervised method based on subset scanning over autoencoder activations. The contributions of our work are threefold. First, we propose a novel method combining detection with reconstruction error and subset scanning scores to improve the anomaly score of current autoencoders without requiring any retraining. Second, we provide the ability to inspect and visualize the set of anomalous nodes in the reconstruction error space that make a sample noised. Third, we show that subset scanning can be used for anomaly detection in the inner layers of the autoencoder. We provide detection power results for several untargeted adversarial noise models under standard 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>



I read the paper and found the approach of treating the adversarial detection problem as anomalous pattern detection quite interesting. The authors do a good job of explaining the relative work, the methods their work builds upon. The analysis provided on various layers (Table 1) is quite insightful. However, I am not convinced by the results and found them hard to compare. The datasets used are also MNIST or Fashion MNIST and doesn*t really capture the real case / challenging scenario for the anomaly detection.
assistant
I read the paper and found the approach of treating the adversarial detection problem as anomalous pattern detection quite interesting. The authors do a good job of explaining the relative work, the methods their work builds upon. The analysis provided on various layers (Table 1) is quite insightful. However, I am not convinced by the results and found them hard to compare. The datasets used are also MNIST or Fashion MNIST and doesn*t really capture the real case / challenging scenario for the anomaly detection.