Towards More Realistic Neural Network Uncertainties
Statistical models are inherently uncertain. Quantifying or at least upper-bounding their uncertainties is vital for safety-critical systems. While standard neural networks do not report this information, several approaches exist to integrate uncertainty estimates into them. Assessing the quality of these uncertainty estimates is not straightforward, as no direct ground truth labels are available. Instead, implicit statistical assessments are required. For regression, we propose to evaluate uncertainty realism---a strict quality criterion---with a Mahalanobis distance-based statistical test. An empirical evaluation reveals the need for uncertainty measures that are appropriate to upper-bound heavy-tailed empirical errors. Alongside, we transfer the variational U-Net classification architecture to standard supervised image-to-image tasks. It provides two uncertainty mechanisms and significantly improves uncertainty realism compared to a plain encoder-decoder model.
The paper makes the following two contributions: 1) a new metric to measure the realism of uncertainty estimates for regression problems which uses a Mahalanobis distance-based statistical test. 2) a new probabilistic architecture for semantic segmentation. Overall I do not think that the paper is qualifies for acceptance, because a) both contributions are only loosely connected and b) some parts are confusingly written or poorly motivated, making the paper hard to follow. After reading the paper it still remains unclear to me why the proposed statistical test is superior to other popular metrics, such as the log-likelihood or the metrics proposed by Mukhoti and Gal. I think the paper would benefit from a more detailed discussion that highlights the differences between the proposed metric and other commonly used metrics. Furthermore, in the experiments, the paper only shows that MC dropout doesn*t achieve realistic uncertainty estimates. I think concluding that variational approaches underestimate the variance is a bit of stretch (see section 4.1) , i e. it would be more convincing if other approaches (e.g Blundell et al. Hernandes-Lobato and Adams) are also considered. The paper also just states that uncertainty estimates obtained by MC dropout are unrealistic but doesn*t elaborates how to improve them. The second contribution, a probabilistic architecture for semantic segmentation, is not introduced in the main paper. Instead details are only provided in the appendix. In my opinion the paper would be easier to follow if it would contain a section that motivates and described the proposed architecture before jumping directly to the experiments. Minor comments: - the acronym FRRN is not defined - Section 4.2 last paragraph: I don*t understand why samples obtain by MC dropout and from the latent space are considered to be from the same distribution? While both methods approximate the weight posterior they use different approximations for that. - In the introduction the paper states that the proposed segmentation network is a U-net like FRRN architecture , however in section 3 it says instead of a U-net based architecture a FRRN based architecture is used. This is somewhat confusing. - Section 4.1: how are the variances scaled for Figure 3 middle? - Section 4.1 It would be also interesting if other metrics, such as log-likelihood or RMSE, to see how well the model is able to fit the data. - I am also surprised that CVAE + MC underperforms to just using CVAE? - Section 4.1: how are the variances scaled for Figure 3 middle? References: Evaluating Bayesian Deep Learning Methods for Semantic Segmentation Jishnu Mukhoti, Yarin Gal arXiv:1811.12709 [cs.CV] Weight Uncertainty in Neural Networks Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra ICML 2015 Hernández-Lobato J. M. and Adams R. Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks, ICML 2015
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Towards More Realistic Neural Network Uncertainties
Statistical models are inherently uncertain. Quantifying or at least upper-bounding their uncertainties is vital for safety-critical systems. While standard neural networks do not report this information, several approaches exist to integrate uncertainty estimates into them. Assessing the quality of these uncertainty estimates is not straightforward, as no direct ground truth labels are available. Instead, implicit statistical assessments are required. For regression, we propose to evaluate uncertainty realism---a strict quality criterion---with a Mahalanobis distance-based statistical test. An empirical evaluation reveals the need for uncertainty measures that are appropriate to upper-bound heavy-tailed empirical errors. Alongside, we transfer the variational U-Net classification architecture to standard supervised image-to-image tasks. It provides two uncertainty mechanisms and significantly improves uncertainty realism compared to a plain encoder-decoder model.
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 makes the following two contributions: 1) a new metric to measure the realism of uncertainty estimates for regression problems which uses a Mahalanobis distance-based statistical test. 2) a new probabilistic architecture for semantic segmentation. Overall I do not think that the paper is qualifies for acceptance, because a) both contributions are only loosely connected and b) some parts are confusingly written or poorly motivated, making the paper hard to follow. After reading the paper it still remains unclear to me why the proposed statistical test is superior to other popular metrics, such as the log-likelihood or the metrics proposed by Mukhoti and Gal. I think the paper would benefit from a more detailed discussion that highlights the differences between the proposed metric and other commonly used metrics. Furthermore, in the experiments, the paper only shows that MC dropout doesn*t achieve realistic uncertainty estimates. I think concluding that variational approaches underestimate the variance is a bit of stretch (see section 4.1) , i e. it would be more convincing if other approaches (e.g Blundell et al. Hernandes-Lobato and Adams) are also considered. The paper also just states that uncertainty estimates obtained by MC dropout are unrealistic but doesn*t elaborates how to improve them. The second contribution, a probabilistic architecture for semantic segmentation, is not introduced in the main paper. Instead details are only provided in the appendix. In my opinion the paper would be easier to follow if it would contain a section that motivates and described the proposed architecture before jumping directly to the experiments. Minor comments: - the acronym FRRN is not defined - Section 4.2 last paragraph: I don*t understand why samples obtain by MC dropout and from the latent space are considered to be from the same distribution? While both methods approximate the weight posterior they use different approximations for that. - In the introduction the paper states that the proposed segmentation network is a U-net like FRRN architecture , however in section 3 it says instead of a U-net based architecture a FRRN based architecture is used. This is somewhat confusing. - Section 4.1: how are the variances scaled for Figure 3 middle? - Section 4.1 It would be also interesting if other metrics, such as log-likelihood or RMSE, to see how well the model is able to fit the data. - I am also surprised that CVAE + MC underperforms to just using CVAE? - Section 4.1: how are the variances scaled for Figure 3 middle? References: Evaluating Bayesian Deep Learning Methods for Semantic Segmentation Jishnu Mukhoti, Yarin Gal arXiv:1811.12709 [cs.CV] Weight Uncertainty in Neural Networks Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra ICML 2015 Hernández-Lobato J. M. and Adams R. Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks, ICML 2015
assistant
The paper makes the following two contributions: 1) a new metric to measure the realism of uncertainty estimates for regression problems which uses a Mahalanobis distance-based statistical test. 2) a new probabilistic architecture for semantic segmentation. Overall I do not think that the paper is qualifies for acceptance, because a) both contributions are only loosely connected and b) some parts are confusingly written or poorly motivated, making the paper hard to follow. After reading the paper it still remains unclear to me why the proposed statistical test is superior to other popular metrics, such as the log-likelihood or the metrics proposed by Mukhoti and Gal. I think the paper would benefit from a more detailed discussion that highlights the differences between the proposed metric and other commonly used metrics. Furthermore, in the experiments, the paper only shows that MC dropout doesn*t achieve realistic uncertainty estimates. I think concluding that variational approaches underestimate the variance is a bit of stretch (see section 4.1) , i e. it would be more convincing if other approaches (e.g Blundell et al. Hernandes-Lobato and Adams) are also considered. The paper also just states that uncertainty estimates obtained by MC dropout are unrealistic but doesn*t elaborates how to improve them. The second contribution, a probabilistic architecture for semantic segmentation, is not introduced in the main paper. Instead details are only provided in the appendix. In my opinion the paper would be easier to follow if it would contain a section that motivates and described the proposed architecture before jumping directly to the experiments. Minor comments: - the acronym FRRN is not defined - Section 4.2 last paragraph: I don*t understand why samples obtain by MC dropout and from the latent space are considered to be from the same distribution? While both methods approximate the weight posterior they use different approximations for that. - In the introduction the paper states that the proposed segmentation network is a U-net like FRRN architecture , however in section 3 it says instead of a U-net based architecture a FRRN based architecture is used. This is somewhat confusing. - Section 4.1: how are the variances scaled for Figure 3 middle? - Section 4.1 It would be also interesting if other metrics, such as log-likelihood or RMSE, to see how well the model is able to fit the data. - I am also surprised that CVAE + MC underperforms to just using CVAE? - Section 4.1: how are the variances scaled for Figure 3 middle? References: Evaluating Bayesian Deep Learning Methods for Semantic Segmentation Jishnu Mukhoti, Yarin Gal arXiv:1811.12709 [cs.CV] Weight Uncertainty in Neural Networks Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra ICML 2015 Hernández-Lobato J. M. and Adams R. Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks, ICML 2015