When Do Variational Autoencoders Know What They Don*t Know?
Recently, the ability of deep generative models to detect outliers has been called into question because of the demonstration that they frequently assign higher probability density to samples from completely different data sets than were used for training. For example, a model trained on CIFAR-10 may counter-intuitively attribute higher likelihood to samples obtained from SVHN. In this work, we closely examine this phenomena in the specific context of variational autoencoders, a commonly-used approach for anomaly detection. In particular, we demonstrate that VAEs, when appropriately designed and trained, are in fact often proficient in differentiating inlier and outlier distributions, e.g., FashionMNIST vs MNIST, CIFAR-10 vs SVHN and CelebA. We describe various mechanisms that mitigate this capability, including the paradoxical necessity of large or unbounded gradients, which have sometimes been observed to occur during training of VAE models.
The paper studies the problem of detecting outliers using generative models (specifically VAEs) in the detail and describes the condition in which VAEs can be used for outlier detection. The paper is well-written and has a very detailed description of its claims. I found the paper insightful and clear overall, and I believe we need more papers like this in the area of deep generative models. I have some questions and comments which I think addressing them can increase the impact of the paper: - The interaction between the encoder/decoder seems to be important based on the latest papers in this area and just assuming “that the encoder network maintains sufficient complexity to produce a reasonably tight variational bound given the decoder” may not be enough for making conclusions about VAEs (see for instance “Tighter Variational Bounds are Not Necessarily Better” by Rainforth et al. 2019). Are there any experiments that you can add to show the robustness of your results with respect to the encoder network. - It was mentioned that for choosing the right capacity “it can be increased until there is a significant gap between train and test NLL values on the inlier data”; however, this was not shown in the experiments. Can you add the columns in Table 1 and Table 2 that after some point there will be a gap between the train and test NLL? - The current version of the paper seems to be ended abruptly and without any summarization of the contributions, it would be nice to have a discussion/conclusion section which discusses the main assumptions and limitations of the arguments in the paper.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
When Do Variational Autoencoders Know What They Don*t Know?
Recently, the ability of deep generative models to detect outliers has been called into question because of the demonstration that they frequently assign higher probability density to samples from completely different data sets than were used for training. For example, a model trained on CIFAR-10 may counter-intuitively attribute higher likelihood to samples obtained from SVHN. In this work, we closely examine this phenomena in the specific context of variational autoencoders, a commonly-used approach for anomaly detection. In particular, we demonstrate that VAEs, when appropriately designed and trained, are in fact often proficient in differentiating inlier and outlier distributions, e.g., FashionMNIST vs MNIST, CIFAR-10 vs SVHN and CelebA. We describe various mechanisms that mitigate this capability, including the paradoxical necessity of large or unbounded gradients, which have sometimes been observed to occur during training of VAE models.
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>



The paper studies the problem of detecting outliers using generative models (specifically VAEs) in the detail and describes the condition in which VAEs can be used for outlier detection. The paper is well-written and has a very detailed description of its claims. I found the paper insightful and clear overall, and I believe we need more papers like this in the area of deep generative models. I have some questions and comments which I think addressing them can increase the impact of the paper: - The interaction between the encoder/decoder seems to be important based on the latest papers in this area and just assuming “that the encoder network maintains sufficient complexity to produce a reasonably tight variational bound given the decoder” may not be enough for making conclusions about VAEs (see for instance “Tighter Variational Bounds are Not Necessarily Better” by Rainforth et al. 2019). Are there any experiments that you can add to show the robustness of your results with respect to the encoder network. - It was mentioned that for choosing the right capacity “it can be increased until there is a significant gap between train and test NLL values on the inlier data”; however, this was not shown in the experiments. Can you add the columns in Table 1 and Table 2 that after some point there will be a gap between the train and test NLL? - The current version of the paper seems to be ended abruptly and without any summarization of the contributions, it would be nice to have a discussion/conclusion section which discusses the main assumptions and limitations of the arguments in the paper.
assistant
The paper studies the problem of detecting outliers using generative models (specifically VAEs) in the detail and describes the condition in which VAEs can be used for outlier detection. The paper is well-written and has a very detailed description of its claims. I found the paper insightful and clear overall, and I believe we need more papers like this in the area of deep generative models. I have some questions and comments which I think addressing them can increase the impact of the paper: - The interaction between the encoder/decoder seems to be important based on the latest papers in this area and just assuming “that the encoder network maintains sufficient complexity to produce a reasonably tight variational bound given the decoder” may not be enough for making conclusions about VAEs (see for instance “Tighter Variational Bounds are Not Necessarily Better” by Rainforth et al. 2019). Are there any experiments that you can add to show the robustness of your results with respect to the encoder network. - It was mentioned that for choosing the right capacity “it can be increased until there is a significant gap between train and test NLL values on the inlier data”; however, this was not shown in the experiments. Can you add the columns in Table 1 and Table 2 that after some point there will be a gap between the train and test NLL? - The current version of the paper seems to be ended abruptly and without any summarization of the contributions, it would be nice to have a discussion/conclusion section which discusses the main assumptions and limitations of the arguments in the paper.