Confidence calibration is a crucial aspect of machine learning models, ensuring that the predicted confidence scores accurately represent the likelihood of correct predictions.
In recent years, Graph Neural Networks (GNNs) have achieved remarkable accuracy, but their trustworthiness remains unexplored. Research has shown that GNNs tend to be under-confident, necessitating confidence calibration. A novel trustworthy GNN model has been proposed, which uses a topology-aware post-hoc calibration function to improve confidence calibration.
Another area of interest is question answering, where traditional calibration evaluation methods may not be effective. A new calibration metric, MacroCE, has been introduced to better capture the model's ability to assign low confidence to wrong predictions and high confidence to correct ones. A new calibration method, ConsCal, has been proposed to improve calibration by considering consistent predictions from multiple model checkpoints.
Recent studies have also focused on confidence calibration in various applications, such as face and kinship verification, object detection, and pretrained transformers. These studies propose different techniques to improve calibration, including regularization, dynamic data pruning, Bayesian confidence calibration, and learning to cascade.
Practical applications of confidence calibration include:
1. Safety-critical applications: Accurate confidence scores can help identify high-risk predictions that require manual inspection, reducing the likelihood of errors in critical systems.
2. Cascade inference systems: Confidence calibration can improve the trade-off between inference accuracy and computational cost, leading to more efficient systems.
3. Decision-making support: Well-calibrated confidence scores can help users make more informed decisions based on the model's predictions, increasing trust in the system.
A company case study involves the use of confidence calibration in object detection for autonomous vehicles. By calibrating confidence scores with respect to image location and box scale, the system can provide more reliable confidence estimates, improving the safety and performance of the vehicle.
In conclusion, confidence calibration is an essential aspect of machine learning models, ensuring that their predictions are trustworthy and reliable. By connecting to broader theories and exploring various applications, researchers can continue to develop more accurate and efficient models for real-world use.
Confidence Calibration
Confidence Calibration Further Reading
1.Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration http://arxiv.org/abs/2109.14285v3 Xiao Wang, Hongrui Liu, Chuan Shi, Cheng Yang2.Re-Examining Calibration: The Case of Question Answering http://arxiv.org/abs/2205.12507v2 Chenglei Si, Chen Zhao, Sewon Min, Jordan Boyd-Graber3.Calibration of Neural Networks http://arxiv.org/abs/2303.10761v1 Ruslan Vasilev, Alexander D'yakonov4.Calibrating Deep Neural Networks using Explicit Regularisation and Dynamic Data Pruning http://arxiv.org/abs/2212.10005v1 Ramya Hebbalaguppe, Rishabh Patra, Tirtharaj Dash, Gautam Shroff, Lovekesh Vig5.Calibrating Deep Neural Network Classifiers on Out-of-Distribution Datasets http://arxiv.org/abs/2006.08914v1 Zhihui Shao, Jianyi Yang, Shaolei Ren6.Bayesian Confidence Calibration for Epistemic Uncertainty Modelling http://arxiv.org/abs/2109.10092v1 Fabian Küppers, Jan Kronenberger, Jonas Schneider, Anselm Haselhoff7.Bag of Tricks for In-Distribution Calibration of Pretrained Transformers http://arxiv.org/abs/2302.06690v1 Jaeyoung Kim, Dongbin Na, Sungchul Choi, Sungbin Lim8.Confidence-Calibrated Face and Kinship Verification http://arxiv.org/abs/2210.13905v2 Min Xu, Ximiao Zhang, Xiuzhuang Zhou9.Learning to Cascade: Confidence Calibration for Improving the Accuracy and Computational Cost of Cascade Inference Systems http://arxiv.org/abs/2104.09286v1 Shohei Enomoto, Takeharu Eda10.Multivariate Confidence Calibration for Object Detection http://arxiv.org/abs/2004.13546v1 Fabian Küppers, Jan Kronenberger, Amirhossein Shantia, Anselm HaselhoffConfidence Calibration Frequently Asked Questions
What is confidence calibration in machine learning?
Confidence calibration is a crucial aspect of machine learning models that ensures the predicted confidence scores accurately represent the likelihood of correct predictions. A well-calibrated model provides reliable estimates of its own performance, which can be useful in various applications, such as safety-critical systems, cascade inference systems, and decision-making support.
Why is confidence calibration important?
Confidence calibration is important because it helps improve the trustworthiness and reliability of machine learning models. Accurate confidence scores can help identify high-risk predictions that require manual inspection, reduce the likelihood of errors in critical systems, improve the trade-off between inference accuracy and computational cost, and help users make more informed decisions based on the model's predictions.
How can confidence calibration be improved in Graph Neural Networks (GNNs)?
A novel trustworthy GNN model has been proposed, which uses a topology-aware post-hoc calibration function to improve confidence calibration. This approach addresses the issue of GNNs being under-confident by adjusting the predicted confidence scores to better represent the likelihood of correct predictions.
What is MacroCE and how does it help in question answering?
MacroCE is a new calibration metric introduced to better capture a model's ability to assign low confidence to wrong predictions and high confidence to correct ones in question answering tasks. Traditional calibration evaluation methods may not be effective in this context, so MacroCE provides a more suitable measure of calibration performance.
What is ConsCal and how does it improve calibration?
ConsCal is a new calibration method proposed to improve confidence calibration by considering consistent predictions from multiple model checkpoints. This approach helps to enhance the model's ability to assign low confidence to wrong predictions and high confidence to correct ones, leading to better overall calibration performance.
What are some techniques to improve confidence calibration in various applications?
Different techniques have been proposed to improve confidence calibration in various applications, such as face and kinship verification, object detection, and pretrained transformers. These techniques include regularization, dynamic data pruning, Bayesian confidence calibration, and learning to cascade.
How can confidence calibration be applied in autonomous vehicles?
In a company case study, confidence calibration was used in object detection for autonomous vehicles. By calibrating confidence scores with respect to image location and box scale, the system can provide more reliable confidence estimates, improving the safety and performance of the vehicle. This practical application demonstrates the importance of confidence calibration in real-world scenarios.
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