Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
Uncertainty estimation and ensembling methods go hand-in-hand. Uncertainty estimation is one of the main benchmarks for assessment of ensembling performance. At the same time, deep learning ensembles have provided state-of-the-art results in uncertainty estimation. In this work, we focus on in-domain uncertainty for image classification. We explore the standards for its quantification and point out pitfalls of existing metrics. Avoiding these pitfalls, we perform a broad study of different ensembling techniques. To provide more insight in the broad comparison, we introduce the deep ensemble equivalent (DEE) and show that many sophisticated ensembling techniques are equivalent to an ensemble of very few independently trained networks in terms of the test log-likelihood.
Summary: This paper mainly concerns the quality of in-domain uncertainty for image classification. After exploring common standards for uncertainty quantification, the authors point out pitfalls of existing metrics by investigating different ensembling techniques and introduce a novel metric called deep ensemble equivalent (DEE) that essentially measures the number of independent models in an ensemble of DNNs. Based on the DEE score, a detailed evaluation of modern DNN ensembles is performed on CIFAR-10/100 and ImageNet datasets. Strengths: The paper is well written and easy to follow. The relationship to previous works is also well described. Overall, I think this is a good paper, which gives a detailed overview of existing metrics for accessing the quality in in-domain uncertainty estimation. The idea behind the proposed DEE score is nice and simple, clearly showing the quality of different ensembling methods (in Fig. 3). Given the importance of uncertainty analysis to deep learning, I believe this work will have a positive impact on the community. Weaknesses: - On page 6, the authors mention that the prior in Eq. 3 is taken to be a Gaussian N(mu, diag(sigma^2)) for Bayesian neural networks, however, many other choices of a prior distribution are available in the literature. What is the impact of changing prior distributions on the quality of uncertainty estimates in the case of variational inference? - Data augmentation is commonly used for improving model performance. However, I find the results presented in Sect 4.3 are not clear enough, note that for a given ensembling method in Table 1, the negative calibrated log-likelihood may increase or decrease when using different networks (VGG, ResNet, etc.). I think it would be interesting to elaborate a bit more on the influence of model complexity. - On page 15, in Eq. 12, the choice of the variance parameter sigma_p^2=1/(N*wd) seems unclear and should be better explained. Minor comments: The size of some figures appears too small, for example Fig. 4 and Fig. 5, which may hinder readability.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
Uncertainty estimation and ensembling methods go hand-in-hand. Uncertainty estimation is one of the main benchmarks for assessment of ensembling performance. At the same time, deep learning ensembles have provided state-of-the-art results in uncertainty estimation. In this work, we focus on in-domain uncertainty for image classification. We explore the standards for its quantification and point out pitfalls of existing metrics. Avoiding these pitfalls, we perform a broad study of different ensembling techniques. To provide more insight in the broad comparison, we introduce the deep ensemble equivalent (DEE) and show that many sophisticated ensembling techniques are equivalent to an ensemble of very few independently trained networks in terms of the test log-likelihood.
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>



Summary: This paper mainly concerns the quality of in-domain uncertainty for image classification. After exploring common standards for uncertainty quantification, the authors point out pitfalls of existing metrics by investigating different ensembling techniques and introduce a novel metric called deep ensemble equivalent (DEE) that essentially measures the number of independent models in an ensemble of DNNs. Based on the DEE score, a detailed evaluation of modern DNN ensembles is performed on CIFAR-10/100 and ImageNet datasets. Strengths: The paper is well written and easy to follow. The relationship to previous works is also well described. Overall, I think this is a good paper, which gives a detailed overview of existing metrics for accessing the quality in in-domain uncertainty estimation. The idea behind the proposed DEE score is nice and simple, clearly showing the quality of different ensembling methods (in Fig. 3). Given the importance of uncertainty analysis to deep learning, I believe this work will have a positive impact on the community. Weaknesses: - On page 6, the authors mention that the prior in Eq. 3 is taken to be a Gaussian N(mu, diag(sigma^2)) for Bayesian neural networks, however, many other choices of a prior distribution are available in the literature. What is the impact of changing prior distributions on the quality of uncertainty estimates in the case of variational inference? - Data augmentation is commonly used for improving model performance. However, I find the results presented in Sect 4.3 are not clear enough, note that for a given ensembling method in Table 1, the negative calibrated log-likelihood may increase or decrease when using different networks (VGG, ResNet, etc.). I think it would be interesting to elaborate a bit more on the influence of model complexity. - On page 15, in Eq. 12, the choice of the variance parameter sigma_p^2=1/(N*wd) seems unclear and should be better explained. Minor comments: The size of some figures appears too small, for example Fig. 4 and Fig. 5, which may hinder readability.
assistant
Summary: This paper mainly concerns the quality of in-domain uncertainty for image classification. After exploring common standards for uncertainty quantification, the authors point out pitfalls of existing metrics by investigating different ensembling techniques and introduce a novel metric called deep ensemble equivalent (DEE) that essentially measures the number of independent models in an ensemble of DNNs. Based on the DEE score, a detailed evaluation of modern DNN ensembles is performed on CIFAR-10/100 and ImageNet datasets. Strengths: The paper is well written and easy to follow. The relationship to previous works is also well described. Overall, I think this is a good paper, which gives a detailed overview of existing metrics for accessing the quality in in-domain uncertainty estimation. The idea behind the proposed DEE score is nice and simple, clearly showing the quality of different ensembling methods (in Fig. 3). Given the importance of uncertainty analysis to deep learning, I believe this work will have a positive impact on the community. Weaknesses: - On page 6, the authors mention that the prior in Eq. 3 is taken to be a Gaussian N(mu, diag(sigma^2)) for Bayesian neural networks, however, many other choices of a prior distribution are available in the literature. What is the impact of changing prior distributions on the quality of uncertainty estimates in the case of variational inference? - Data augmentation is commonly used for improving model performance. However, I find the results presented in Sect 4.3 are not clear enough, note that for a given ensembling method in Table 1, the negative calibrated log-likelihood may increase or decrease when using different networks (VGG, ResNet, etc.). I think it would be interesting to elaborate a bit more on the influence of model complexity. - On page 15, in Eq. 12, the choice of the variance parameter sigma_p^2=1/(N*wd) seems unclear and should be better explained. Minor comments: The size of some figures appears too small, for example Fig. 4 and Fig. 5, which may hinder readability.