Empirical confidence estimates for classification by deep neural networks
How well can we estimate the probability that the classification predicted by a deep neural network is correct (or in the Top 5)? It is well-known that the softmax values of the network are not estimates of the probabilities of class labels. However, there is a misconception that these values are not informative. We define the notion of implied loss and prove that if an uncertainty measure is an implied loss, then low uncertainty means high probability of correct (or Top-k) classification on the test set. We demonstrate empirically that these values can be used to measure the confidence that the classification is correct. Our method is simple to use on existing networks: we proposed confidence measures for Top-k which can be evaluated by binning values on the test set.
This paper shows that the maximum softmax probability is useful for uncertainty estimation on in-class data and not just for detecting out-of-distribution data. They argue this with the Bayes Ratio, which here is an uncertainty estimation quality measure that seems worth exploring more for assessing the quality of uncertainty estimation techniques. The community is still in need of a good uncertainty estimation measure (Brier score is too tangled with accuracy, has low numerical resolution, and hardly penalizes consistent overconfidence; AUROC for error detection doesn*t budge; the Soft-F1 score is without theoretical motivation; area under the response rate recall curve is too close to 1 when accuracy is high), and this could be it. They evaluate on ImageNet-1K, which most uncertainty estimation papers fail to consider. By considering CIFAR-10, CIFAR-100, and ImageNet, we get fuller picture of the ranking of the utility of many uncertainty estimators, which is somewhat important for the community. This paper is currently borderline, in that what it proposes is simple, but perhaps too simple. Its empirical contributions are fairly minimal, though its proposal of the Bayes Ratio for uncertainty estimation quality assessment could quite impactful. Smaller Notes: Figure 1 should be on page 3 not 2. Some content in Appendix B should be incorporated in Section 5.1 > In the odds have increased If B.3 should have a worked example in our setting as well Eqn (19) should say > 65 not <65 Notation of Y is confusing when it means a binning of U. Perhaps use *B*? Is the histogram adaptively computed? Is that what “chosen to have equal weight” means? Perhaps compare the Bayes ratio with RMS Calibration Error, AURRA, or AUROC for error detection. Update: The other reviewers are concerned about lack of clarity which is separate from why I like the paper.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Empirical confidence estimates for classification by deep neural networks
How well can we estimate the probability that the classification predicted by a deep neural network is correct (or in the Top 5)? It is well-known that the softmax values of the network are not estimates of the probabilities of class labels. However, there is a misconception that these values are not informative. We define the notion of implied loss and prove that if an uncertainty measure is an implied loss, then low uncertainty means high probability of correct (or Top-k) classification on the test set. We demonstrate empirically that these values can be used to measure the confidence that the classification is correct. Our method is simple to use on existing networks: we proposed confidence measures for Top-k which can be evaluated by binning values on the test set.
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 shows that the maximum softmax probability is useful for uncertainty estimation on in-class data and not just for detecting out-of-distribution data. They argue this with the Bayes Ratio, which here is an uncertainty estimation quality measure that seems worth exploring more for assessing the quality of uncertainty estimation techniques. The community is still in need of a good uncertainty estimation measure (Brier score is too tangled with accuracy, has low numerical resolution, and hardly penalizes consistent overconfidence; AUROC for error detection doesn*t budge; the Soft-F1 score is without theoretical motivation; area under the response rate recall curve is too close to 1 when accuracy is high), and this could be it. They evaluate on ImageNet-1K, which most uncertainty estimation papers fail to consider. By considering CIFAR-10, CIFAR-100, and ImageNet, we get fuller picture of the ranking of the utility of many uncertainty estimators, which is somewhat important for the community. This paper is currently borderline, in that what it proposes is simple, but perhaps too simple. Its empirical contributions are fairly minimal, though its proposal of the Bayes Ratio for uncertainty estimation quality assessment could quite impactful. Smaller Notes: Figure 1 should be on page 3 not 2. Some content in Appendix B should be incorporated in Section 5.1 > In the odds have increased If B.3 should have a worked example in our setting as well Eqn (19) should say > 65 not <65 Notation of Y is confusing when it means a binning of U. Perhaps use *B*? Is the histogram adaptively computed? Is that what “chosen to have equal weight” means? Perhaps compare the Bayes ratio with RMS Calibration Error, AURRA, or AUROC for error detection. Update: The other reviewers are concerned about lack of clarity which is separate from why I like the paper.
assistant
This paper shows that the maximum softmax probability is useful for uncertainty estimation on in-class data and not just for detecting out-of-distribution data. They argue this with the Bayes Ratio, which here is an uncertainty estimation quality measure that seems worth exploring more for assessing the quality of uncertainty estimation techniques. The community is still in need of a good uncertainty estimation measure (Brier score is too tangled with accuracy, has low numerical resolution, and hardly penalizes consistent overconfidence; AUROC for error detection doesn*t budge; the Soft-F1 score is without theoretical motivation; area under the response rate recall curve is too close to 1 when accuracy is high), and this could be it. They evaluate on ImageNet-1K, which most uncertainty estimation papers fail to consider. By considering CIFAR-10, CIFAR-100, and ImageNet, we get fuller picture of the ranking of the utility of many uncertainty estimators, which is somewhat important for the community. This paper is currently borderline, in that what it proposes is simple, but perhaps too simple. Its empirical contributions are fairly minimal, though its proposal of the Bayes Ratio for uncertainty estimation quality assessment could quite impactful. Smaller Notes: Figure 1 should be on page 3 not 2. Some content in Appendix B should be incorporated in Section 5.1 > In the odds have increased If B.3 should have a worked example in our setting as well Eqn (19) should say > 65 not <65 Notation of Y is confusing when it means a binning of U. Perhaps use *B*? Is the histogram adaptively computed? Is that what “chosen to have equal weight” means? Perhaps compare the Bayes ratio with RMS Calibration Error, AURRA, or AUROC for error detection. Update: The other reviewers are concerned about lack of clarity which is separate from why I like the paper.