Pretraining with random noise for uncertainty calibration

• URL: http://arxiv.org/abs/2412.17411v1
• Date: Mon, 23 Dec 2024 09:22:00 GMT
• Title: Pretraining with random noise for uncertainty calibration
• Authors: Jeonghwan Cheon, Se-Bum Paik,
• Abstract summary: We demonstrate that uncertainty calibration can be effectively achieved through a pretraining method inspired by developmental neuroscience. We show that randomly untrained networks tend to exhibit erroneously high confidence, but pretraining with random noise effectively calibrates these networks. As a result, networks pretrained with random noise exhibit optimal calibration, with confidence closely aligned with accuracy throughout subsequent data training.
• Score: 0.0
• License:
• Abstract: Uncertainty calibration, the process of aligning confidence with accuracy, is a hallmark of human intelligence. However, most machine learning models struggle to achieve this alignment, particularly when the training dataset is small relative to the network's capacity. Here, we demonstrate that uncertainty calibration can be effectively achieved through a pretraining method inspired by developmental neuroscience. Specifically, training with random noise before data training allows neural networks to calibrate their uncertainty, ensuring that confidence levels are aligned with actual accuracy. We show that randomly initialized, untrained networks tend to exhibit erroneously high confidence, but pretraining with random noise effectively calibrates these networks, bringing their confidence down to chance levels across input spaces. As a result, networks pretrained with random noise exhibit optimal calibration, with confidence closely aligned with accuracy throughout subsequent data training. These pre-calibrated networks also perform better at identifying "unknown data" by exhibiting lower confidence for out-of-distribution samples. Our findings provide a fundamental solution for uncertainty calibration in both in-distribution and out-of-distribution contexts.

Related papers

• Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning [53.42244686183879]
  Conformal prediction provides model-agnostic and distribution-free uncertainty quantification. Yet, conformal prediction is not reliable under poisoning attacks where adversaries manipulate both training and calibration data. We propose reliable prediction sets (RPS): the first efficient method for constructing conformal prediction sets with provable reliability guarantees under poisoning.
  arXiv  Detail & Related papers  (2024-10-13T15:37:11Z)
• Confidence Intervals and Simultaneous Confidence Bands Based on Deep Learning [0.36832029288386137]
  We provide a valid non-parametric bootstrap method that correctly disentangles data uncertainty from the noise inherent in the adopted optimization algorithm. The proposed ad-hoc method can be easily integrated into any deep neural network without interfering with the training process.
  arXiv  Detail & Related papers  (2024-06-20T05:51:37Z)
• Learning Sample Difficulty from Pre-trained Models for Reliable Prediction [55.77136037458667]
  We propose to utilize large-scale pre-trained models to guide downstream model training with sample difficulty-aware entropy regularization. We simultaneously improve accuracy and uncertainty calibration across challenging benchmarks.
  arXiv  Detail & Related papers  (2023-04-20T07:29:23Z)
• Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection [58.789823426981044]
  We propose a novel auxiliary loss formulation that aims to align the class confidence of bounding boxes with the accurateness of predictions. Our results reveal that our train-time loss surpasses strong calibration baselines in reducing calibration error for both in and out-domain scenarios.
  arXiv  Detail & Related papers  (2023-03-25T08:56:21Z)
• Bayesian Neural Network Versus Ex-Post Calibration For Prediction Uncertainty [0.2343856409260935]
  Probabilistic predictions from neural networks account for predictive uncertainty during classification. In practice most datasets are trained on non-probabilistic neural networks which by default do not capture this inherent uncertainty. A plausible alternative to the calibration approach is to use Bayesian neural networks, which directly models a predictive distribution.
  arXiv  Detail & Related papers  (2022-09-29T07:22:19Z)
• 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)
• Conformal prediction for the design problem [72.14982816083297]
  In many real-world deployments of machine learning, we use a prediction algorithm to choose what data to test next. In such settings, there is a distinct type of distribution shift between the training and test data. We introduce a method to quantify predictive uncertainty in such settings.
  arXiv  Detail & Related papers  (2022-02-08T02:59:12Z)
• 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 Uncertainty Calibration via Prior Augmented Data [56.88185136509654]
  Neural networks have proven successful at learning from complex data distributions by acting as universal function approximators. They are often overconfident in their predictions, which leads to inaccurate and miscalibrated probabilistic predictions. We propose a solution by seeking out regions of feature space where the model is unjustifiably overconfident, and conditionally raising the entropy of those predictions towards that of the prior distribution of the labels.
  arXiv  Detail & Related papers  (2021-02-22T07:02:37Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)

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.