Two Sides of Miscalibration: Identifying Over and Under-Confidence Prediction for Network Calibration

• URL: http://arxiv.org/abs/2308.03172v1
• Date: Sun, 6 Aug 2023 17:59:14 GMT
• Title: Two Sides of Miscalibration: Identifying Over and Under-Confidence Prediction for Network Calibration
• Authors: Shuang Ao, Stefan Rueger, Advaith Siddharthan
• Abstract summary: Proper confidence calibration of deep neural networks is essential for reliable predictions in safety-critical tasks. Miscalibration can lead to model over-confidence and/or under-confidence. We introduce a novel metric, a miscalibration score, to identify the overall and class-wise calibration status. We use the class-wise miscalibration score as a proxy to design a calibration technique that can tackle both over and under-confidence.
• Score: 1.192436948211501
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Proper confidence calibration of deep neural networks is essential for reliable predictions in safety-critical tasks. Miscalibration can lead to model over-confidence and/or under-confidence; i.e., the model's confidence in its prediction can be greater or less than the model's accuracy. Recent studies have highlighted the over-confidence issue by introducing calibration techniques and demonstrated success on various tasks. However, miscalibration through under-confidence has not yet to receive much attention. In this paper, we address the necessity of paying attention to the under-confidence issue. We first introduce a novel metric, a miscalibration score, to identify the overall and class-wise calibration status, including being over or under-confident. Our proposed metric reveals the pitfalls of existing calibration techniques, where they often overly calibrate the model and worsen under-confident predictions. Then we utilize the class-wise miscalibration score as a proxy to design a calibration technique that can tackle both over and under-confidence. We report extensive experiments that show our proposed methods substantially outperforming existing calibration techniques. We also validate our proposed calibration technique on an automatic failure detection task with a risk-coverage curve, reporting that our methods improve failure detection as well as trustworthiness of the model. The code are available at \url{https://github.com/AoShuang92/miscalibration_TS}.

Related papers

• Towards Certification of Uncertainty Calibration under Adversarial Attacks [96.48317453951418]
  We show that attacks can significantly harm calibration, and thus propose certified calibration as worst-case bounds on calibration under adversarial perturbations. We propose novel calibration attacks and demonstrate how they can improve model calibration through textitadversarial calibration training
  arXiv  Detail & Related papers  (2024-05-22T18:52:09Z)
• Revisiting Confidence Estimation: Towards Reliable Failure Prediction [53.79160907725975]
  We find a general, widely existing but actually-neglected phenomenon that most confidence estimation methods are harmful for detecting misclassification errors. We propose to enlarge the confidence gap by finding flat minima, which yields state-of-the-art failure prediction performance.
  arXiv  Detail & Related papers  (2024-03-05T11:44:14Z)
• Towards Calibrated Deep Clustering Network [60.71776081164377]
  In deep clustering, the estimated confidence for a sample belonging to a particular cluster greatly exceeds its actual prediction accuracy. We propose a novel dual-head (calibration head and clustering head) deep clustering model that can effectively calibrate the estimated confidence and the actual accuracy. Extensive experiments demonstrate the proposed calibrated deep clustering model not only surpasses state-of-the-art deep clustering methods by 10 times in terms of expected calibration error but also significantly outperforms them in terms of clustering accuracy.
  arXiv  Detail & Related papers  (2024-03-04T11:23:40Z)
• Calibration by Distribution Matching: Trainable Kernel Calibration Metrics [56.629245030893685]
  We introduce kernel-based calibration metrics that unify and generalize popular forms of calibration for both classification and regression. These metrics admit differentiable sample estimates, making it easy to incorporate a calibration objective into empirical risk minimization. We provide intuitive mechanisms to tailor calibration metrics to a decision task, and enforce accurate loss estimation and no regret decisions.
  arXiv  Detail & Related papers  (2023-10-31T06:19:40Z)
• Calibration of Neural Networks [77.34726150561087]
  This paper presents a survey of confidence calibration problems in the context of neural networks. We analyze problem statement, calibration definitions, and different approaches to evaluation. Empirical experiments cover various datasets and models, comparing calibration methods according to different criteria.
  arXiv  Detail & Related papers  (2023-03-19T20:27:51Z)
• On Calibrating Semantic Segmentation Models: Analyses and An Algorithm [51.85289816613351]
  We study the problem of semantic segmentation calibration. Model capacity, crop size, multi-scale testing, and prediction correctness have impact on calibration. We propose a simple, unifying, and effective approach, namely selective scaling.
  arXiv  Detail & Related papers  (2022-12-22T22:05:16Z)
• Confidence Calibration for Intent Detection via Hyperspherical Space and Rebalanced Accuracy-Uncertainty Loss [17.26964140836123]
  In some scenarios, users do not only care about the accuracy but also the confidence of model. We propose a model using the hyperspherical space and rebalanced accuracy-uncertainty loss. Our model outperforms the existing calibration methods and achieves a significant improvement on the calibration metric.
  arXiv  Detail & Related papers  (2022-03-17T12:01:33Z)
• Bayesian Confidence Calibration for Epistemic Uncertainty Modelling [4.358626952482686]
  We introduce a framework to obtain confidence estimates in conjunction with an uncertainty of the calibration method. We achieve state-of-the-art calibration performance for object detection calibration.
  arXiv  Detail & Related papers  (2021-09-21T10:53:16Z)
• Improving model calibration with accuracy versus uncertainty optimization [17.056768055368384]
  A well-calibrated model should be accurate when it is certain about its prediction and indicate high uncertainty when it is likely to be inaccurate. We propose an optimization method that leverages the relationship between accuracy and uncertainty as an anchor for uncertainty calibration. We demonstrate our approach with mean-field variational inference and compare with state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-12-14T20:19:21Z)
• Multivariate Confidence Calibration for Object Detection [7.16879432974126]
  We present a novel framework to measure and calibrate biased confidence estimates of object detection methods. Our approach allows, for the first time, to obtain calibrated confidence estimates with respect to image location and box scale. We show that our developed methods outperform state-of-the-art calibration models for the task of object detection.
  arXiv  Detail & Related papers  (2020-04-28T14:17:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.