Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks

• URL: http://arxiv.org/abs/2202.07679v1
• Date: Tue, 15 Feb 2022 19:04:05 GMT
• Title: Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks
• Authors: Zhen Lin, Shubhendu Trivedi, Jimeng Sun
• Abstract summary: This paper proposes a new Kernel-based calibration method called KCal. Unlike other calibration procedures, KCal does not operate directly on the logits or softmax outputs of the DNN. In effect, KCal amounts to a supervised dimensionality reduction of the neural network embedding.
• Score: 40.282423098764404
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep neural network (DNN) classifiers are often overconfident, producing miscalibrated class probabilities. Most existing calibration methods either lack theoretical guarantees for producing calibrated outputs or reduce the classification accuracy in the process. This paper proposes a new Kernel-based calibration method called KCal. Unlike other calibration procedures, KCal does not operate directly on the logits or softmax outputs of the DNN. Instead, it uses the penultimate-layer latent embedding to train a metric space in a supervised manner. In effect, KCal amounts to a supervised dimensionality reduction of the neural network embedding, and generates a prediction using kernel density estimation on a holdout calibration set. We first analyze KCal theoretically, showing that it enjoys a provable asymptotic calibration guarantee. Then, through extensive experiments, we confirm that KCal consistently outperforms existing calibration methods in terms of both the classification accuracy and the (confidence and class-wise) calibration error.

Related papers

• Feature Clipping for Uncertainty Calibration [24.465567005078135]
  Modern deep neural networks (DNNs) often suffer from overconfidence, leading to miscalibration. We propose a novel post-hoc calibration method called feature clipping (FC) to address this issue. FC involves clipping feature values to a specified threshold, effectively increasing entropy in high calibration error samples.
  arXiv  Detail & Related papers  (2024-10-16T06:44:35Z)
• ForeCal: Random Forest-based Calibration for DNNs [0.0]
  We propose ForeCal, a novel post-hoc calibration algorithm based on Random forests. ForeCal exploits two unique properties of Random forests: the ability to enforce weak monotonicity and range-preservation. We show that ForeCal outperforms existing methods in terms of Expected Error(ECE) with minimal impact on the discriminative power of the base as measured by AUC.
  arXiv  Detail & Related papers  (2024-09-04T04:56:41Z)
• 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)
• A Consistent and Differentiable Lp Canonical Calibration Error Estimator [21.67616079217758]
  Deep neural networks are poorly calibrated and tend to output overconfident predictions. We propose a low-bias, trainable calibration error estimator based on Dirichlet kernel density estimates. Our method has a natural choice of kernel, and can be used to generate consistent estimates of other quantities.
  arXiv  Detail & Related papers  (2022-10-13T15:11:11Z)
• T-Cal: An optimal test for the calibration of predictive models [49.11538724574202]
  We consider detecting mis-calibration of predictive models using a finite validation dataset as a hypothesis testing problem. detecting mis-calibration is only possible when the conditional probabilities of the classes are sufficiently smooth functions of the predictions. We propose T-Cal, a minimax test for calibration based on a de-biased plug-in estimator of the $ell$-Expected Error (ECE)
  arXiv  Detail & Related papers  (2022-03-03T16:58:54Z)
• Meta-Cal: Well-controlled Post-hoc Calibration by Ranking [23.253020991581963]
  Post-hoc calibration is a technique to recalibrate a model, and its goal is to learn a calibration map. Existing approaches mostly focus on constructing calibration maps with low calibration errors. We study post-hoc calibration for multi-class classification under constraints, as a calibrator with a low calibration error does not necessarily mean it is useful in practice.
  arXiv  Detail & Related papers  (2021-05-10T12:00:54Z)
• Unsupervised Calibration under Covariate Shift [92.02278658443166]
  We introduce the problem of calibration under domain shift and propose an importance sampling based approach to address it. We evaluate and discuss the efficacy of our method on both real-world datasets and synthetic datasets.
  arXiv  Detail & Related papers  (2020-06-29T21:50:07Z)
• Post-hoc Calibration of Neural Networks by g-Layers [51.42640515410253]
  In recent years, there is a surge of research on neural network calibration. It is known that minimizing Negative Log-Likelihood (NLL) will lead to a calibrated network on the training set if the global optimum is attained. We prove that even though the base network ($f$) does not lead to the global optimum of NLL, by adding additional layers ($g$) and minimizing NLL by optimizing the parameters of $g$ one can obtain a calibrated network.
  arXiv  Detail & Related papers  (2020-06-23T07:55:10Z)
• Calibration of Neural Networks using Splines [51.42640515410253]
  Measuring calibration error amounts to comparing two empirical distributions. We introduce a binning-free calibration measure inspired by the classical Kolmogorov-Smirnov (KS) statistical test. Our method consistently outperforms existing methods on KS error as well as other commonly used calibration measures.
  arXiv  Detail & Related papers  (2020-06-23T07:18:05Z)

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.