Offline Deep models calibration with bayesian neural networks
We apply Bayesian Neural Networks to improve calibration of state-of-the-art deep neural networks. We show that, even with the most basic amortized approximate posterior distribution, and fast fully connected neural network for the likelihood, the Bayesian framework clearly outperforms other simple maximum likelihood based solutions that have recently shown very good performance, as temperature scaling. As an example, we reduce the Expected Calibration Error (ECE) from 0.52 to 0.24 on CIFAR-10 and from 4.28 to 2.456 on CIFAR-100 on two Wide ResNet with 96.13% and 80.39% accuracy respectively, which are among the best results published for this task. We demonstrate our robustness and performance with experiments on a wide set of state-of-the-art computer vision models. Moreover, our approach acts off-line, and thus can be applied to any probabilistic model regardless of the limitations that the model may present during training. This make it suitable to calibrate systems that make use of pre-trained deep neural networks that are expensive to train for a specific task, or to directly train a calibrated deep convolutional model with Monte Carlo Dropout approximations, among others. However, our method is still complementary with any Bayesian Neural Network for further improvement.
 This paper presents an approach for calibrating the predictions of deep neural networks. The idea is quite simple and straightforward - simply use a more expressive model (Bayesian neural network with amortized inference). Surprisingly, the results show that this simple approach outperforms many of the recent approaches, such as those based on temperature scaling. A trick that they use is to control the KL term in the approximation of the ELBO using a hyperparameter (but this has been used in prior work on Bayesian neural nets). Overall, the idea as such is not that novel (just applying a Bayesian neural network with amortized inference) but the results look quite impressive. However, although the paper seems to advocate that a simple Bayesian neural network is enough to get well-calibrated probabilistic predictions, recent work has shown that even Bayesian uncertainties may be inaccurate, especially in case of model mis-specifications or due to the use of approximate inference. For example, see "Accurate Uncertainties for Deep Learning Using Calibrated Regression" ( Kuleshov et al, 2018). It is quite surprising that the proposed approach works so well but there isn*t much of an insight as to why it works well. Is it the amortized inference that helps, or something else? I think a more detailed analysis needs to be done. Even some empirical analysis that, for example, shows that using MCMC gives inferior results than amortized inference would help here. Besides, I would also like to point out that there is some recent work on trainable calibration measures. See "Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings" (Kumar et al, 2018). It would be good to discuss this. Overall, the paper has a rather straightforward idea which seems to give good results. However, it doesn*t offer any new insights as to why it works, especially since recent work, such as the one I mentioned above, has shown that taking a simple Bayesian approach that provides uncertainty estimates doesn*t quite address the problem being studied here.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Offline Deep models calibration with bayesian neural networks
We apply Bayesian Neural Networks to improve calibration of state-of-the-art deep neural networks. We show that, even with the most basic amortized approximate posterior distribution, and fast fully connected neural network for the likelihood, the Bayesian framework clearly outperforms other simple maximum likelihood based solutions that have recently shown very good performance, as temperature scaling. As an example, we reduce the Expected Calibration Error (ECE) from 0.52 to 0.24 on CIFAR-10 and from 4.28 to 2.456 on CIFAR-100 on two Wide ResNet with 96.13% and 80.39% accuracy respectively, which are among the best results published for this task. We demonstrate our robustness and performance with experiments on a wide set of state-of-the-art computer vision models. Moreover, our approach acts off-line, and thus can be applied to any probabilistic model regardless of the limitations that the model may present during training. This make it suitable to calibrate systems that make use of pre-trained deep neural networks that are expensive to train for a specific task, or to directly train a calibrated deep convolutional model with Monte Carlo Dropout approximations, among others. However, our method is still complementary with any Bayesian Neural Network for further improvement.
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 presents an approach for calibrating the predictions of deep neural networks. The idea is quite simple and straightforward - simply use a more expressive model (Bayesian neural network with amortized inference). Surprisingly, the results show that this simple approach outperforms many of the recent approaches, such as those based on temperature scaling. A trick that they use is to control the KL term in the approximation of the ELBO using a hyperparameter (but this has been used in prior work on Bayesian neural nets). Overall, the idea as such is not that novel (just applying a Bayesian neural network with amortized inference) but the results look quite impressive. However, although the paper seems to advocate that a simple Bayesian neural network is enough to get well-calibrated probabilistic predictions, recent work has shown that even Bayesian uncertainties may be inaccurate, especially in case of model mis-specifications or due to the use of approximate inference. For example, see "Accurate Uncertainties for Deep Learning Using Calibrated Regression" ( Kuleshov et al, 2018). It is quite surprising that the proposed approach works so well but there isn*t much of an insight as to why it works well. Is it the amortized inference that helps, or something else? I think a more detailed analysis needs to be done. Even some empirical analysis that, for example, shows that using MCMC gives inferior results than amortized inference would help here. Besides, I would also like to point out that there is some recent work on trainable calibration measures. See "Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings" (Kumar et al, 2018). It would be good to discuss this. Overall, the paper has a rather straightforward idea which seems to give good results. However, it doesn*t offer any new insights as to why it works, especially since recent work, such as the one I mentioned above, has shown that taking a simple Bayesian approach that provides uncertainty estimates doesn*t quite address the problem being studied here.
assistant
 This paper presents an approach for calibrating the predictions of deep neural networks. The idea is quite simple and straightforward - simply use a more expressive model (Bayesian neural network with amortized inference). Surprisingly, the results show that this simple approach outperforms many of the recent approaches, such as those based on temperature scaling. A trick that they use is to control the KL term in the approximation of the ELBO using a hyperparameter (but this has been used in prior work on Bayesian neural nets). Overall, the idea as such is not that novel (just applying a Bayesian neural network with amortized inference) but the results look quite impressive. However, although the paper seems to advocate that a simple Bayesian neural network is enough to get well-calibrated probabilistic predictions, recent work has shown that even Bayesian uncertainties may be inaccurate, especially in case of model mis-specifications or due to the use of approximate inference. For example, see "Accurate Uncertainties for Deep Learning Using Calibrated Regression" ( Kuleshov et al, 2018). It is quite surprising that the proposed approach works so well but there isn*t much of an insight as to why it works well. Is it the amortized inference that helps, or something else? I think a more detailed analysis needs to be done. Even some empirical analysis that, for example, shows that using MCMC gives inferior results than amortized inference would help here. Besides, I would also like to point out that there is some recent work on trainable calibration measures. See "Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings" (Kumar et al, 2018). It would be good to discuss this. Overall, the paper has a rather straightforward idea which seems to give good results. However, it doesn*t offer any new insights as to why it works, especially since recent work, such as the one I mentioned above, has shown that taking a simple Bayesian approach that provides uncertainty estimates doesn*t quite address the problem being studied here.