Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning
Identifying anomalous samples from highly complex and unstructured data is a crucial but challenging task in a variety of intelligent systems. In this paper, we present a novel deep anomaly detection framework named AnoDM (standing for Anomaly detection based on unsupervised Disentangled representation learning and Manifold learning). The disentanglement learning is currently implemented by beta-VAE for automatically discovering interpretable factorized latent representations in a completely unsupervised manner. The manifold learning is realized by t-SNE for projecting the latent representations to a 2D map. We define a new anomaly score function by combining beta-VAE*s reconstruction error in the raw feature space and local density estimation in the t-SNE space. AnoDM was evaluated on both image and time-series data and achieved better results than models that use just one of the two measures and other deep learning methods.
This paper proposes to combine beta-VAE and t-SNE for anomaly detection. Although the problem and the proposed approach is relevant, I have the following concerns. - The problem setting is not well explained. In particular, it is not clear whether the setting is unsupervised or not. It seems that the proposed method is for unsupervised anomaly detection. However, the authors mention that beta-VAE is trained on normal data, which means that it is not unsupervised. Please clarify this point. - The originality and the technical quality of the proposed method is not high as it is a straightforward combination of two existing method. If the proposed combination has some theoretical advantage for anomaly detection, the paper becomes more interesting. However, there is no theoretical analysis of the proposed method, hence the significance of the contribution is hot high in its current state. - Experimental results are not convincing. * The authors argue that the t-SNE step is important as the proposed method is better than the naive beta-VAE. However, Figure 2 shows that in most cases the score becomes better as alpha gets larger and shows the best score if alpha = 1. From Equation (2), this means that the t-SNE step does not contribute to the anomaly detection performance. This inconsistency should be carefully discussed. * Also, Figure 2 shows that beta should be small and the best score is achieved when beta = 0 in most cases. This means that representation learning is not meaningful and the raw representation (feature vectors) may be already effective for anomaly detection. Hence the significance of the proposed method is not convincing.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning
Identifying anomalous samples from highly complex and unstructured data is a crucial but challenging task in a variety of intelligent systems. In this paper, we present a novel deep anomaly detection framework named AnoDM (standing for Anomaly detection based on unsupervised Disentangled representation learning and Manifold learning). The disentanglement learning is currently implemented by beta-VAE for automatically discovering interpretable factorized latent representations in a completely unsupervised manner. The manifold learning is realized by t-SNE for projecting the latent representations to a 2D map. We define a new anomaly score function by combining beta-VAE*s reconstruction error in the raw feature space and local density estimation in the t-SNE space. AnoDM was evaluated on both image and time-series data and achieved better results than models that use just one of the two measures and other deep learning 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 proposes to combine beta-VAE and t-SNE for anomaly detection. Although the problem and the proposed approach is relevant, I have the following concerns. - The problem setting is not well explained. In particular, it is not clear whether the setting is unsupervised or not. It seems that the proposed method is for unsupervised anomaly detection. However, the authors mention that beta-VAE is trained on normal data, which means that it is not unsupervised. Please clarify this point. - The originality and the technical quality of the proposed method is not high as it is a straightforward combination of two existing method. If the proposed combination has some theoretical advantage for anomaly detection, the paper becomes more interesting. However, there is no theoretical analysis of the proposed method, hence the significance of the contribution is hot high in its current state. - Experimental results are not convincing. * The authors argue that the t-SNE step is important as the proposed method is better than the naive beta-VAE. However, Figure 2 shows that in most cases the score becomes better as alpha gets larger and shows the best score if alpha = 1. From Equation (2), this means that the t-SNE step does not contribute to the anomaly detection performance. This inconsistency should be carefully discussed. * Also, Figure 2 shows that beta should be small and the best score is achieved when beta = 0 in most cases. This means that representation learning is not meaningful and the raw representation (feature vectors) may be already effective for anomaly detection. Hence the significance of the proposed method is not convincing.
assistant
This paper proposes to combine beta-VAE and t-SNE for anomaly detection. Although the problem and the proposed approach is relevant, I have the following concerns. - The problem setting is not well explained. In particular, it is not clear whether the setting is unsupervised or not. It seems that the proposed method is for unsupervised anomaly detection. However, the authors mention that beta-VAE is trained on normal data, which means that it is not unsupervised. Please clarify this point. - The originality and the technical quality of the proposed method is not high as it is a straightforward combination of two existing method. If the proposed combination has some theoretical advantage for anomaly detection, the paper becomes more interesting. However, there is no theoretical analysis of the proposed method, hence the significance of the contribution is hot high in its current state. - Experimental results are not convincing. * The authors argue that the t-SNE step is important as the proposed method is better than the naive beta-VAE. However, Figure 2 shows that in most cases the score becomes better as alpha gets larger and shows the best score if alpha = 1. From Equation (2), this means that the t-SNE step does not contribute to the anomaly detection performance. This inconsistency should be carefully discussed. * Also, Figure 2 shows that beta should be small and the best score is achieved when beta = 0 in most cases. This means that representation learning is not meaningful and the raw representation (feature vectors) may be already effective for anomaly detection. Hence the significance of the proposed method is not convincing.