On the Role of Dataset Quality and Heterogeneity in Model Confidence

• URL: http://arxiv.org/abs/2002.09831v1
• Date: Sun, 23 Feb 2020 05:13:12 GMT
• Title: On the Role of Dataset Quality and Heterogeneity in Model Confidence
• Authors: Yuan Zhao, Jiasi Chen, Samet Oymak
• Abstract summary: Safety-critical applications require machine learning models that output accurate and calibrated probabilities. Uncalibrated deep networks are known to make over-confident predictions. We study the impact of dataset quality by studying the impact of dataset size and the label noise on the model confidence.
• Score: 27.657631193015252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safety-critical applications require machine learning models that output accurate and calibrated probabilities. While uncalibrated deep networks are known to make over-confident predictions, it is unclear how model confidence is impacted by the variations in the data, such as label noise or class size. In this paper, we investigate the role of the dataset quality by studying the impact of dataset size and the label noise on the model confidence. We theoretically explain and experimentally demonstrate that, surprisingly, label noise in the training data leads to under-confident networks, while reduced dataset size leads to over-confident models. We then study the impact of dataset heterogeneity, where data quality varies across classes, on model confidence. We demonstrate that this leads to heterogenous confidence/accuracy behavior in the test data and is poorly handled by the standard calibration algorithms. To overcome this, we propose an intuitive heterogenous calibration technique and show that the proposed approach leads to improved calibration metrics (both average and worst-case errors) on the CIFAR datasets.

Related papers

• CALICO: Confident Active Learning with Integrated Calibration [11.978551396144532]
  We propose an AL framework that self-calibrates the confidence used for sample selection during the training process. We show improved classification performance compared to a softmax-based classifier with fewer labeled samples.
  arXiv  Detail & Related papers  (2024-07-02T15:05:19Z)
• The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness [50.52507648690234]
  Federated learning has the risk of skewing fine-tuning features and compromising the robustness of the model. We introduce three robustness indicators and conduct experiments across diverse robust datasets. Our approach markedly enhances the robustness across diverse scenarios, encompassing various parameter-efficient fine-tuning methods.
  arXiv  Detail & Related papers  (2024-01-25T09:18:51Z)
• Binary Classification with Confidence Difference [100.08818204756093]
  This paper delves into a novel weakly supervised binary classification problem called confidence-difference (ConfDiff) classification. We propose a risk-consistent approach to tackle this problem and show that the estimation error bound the optimal convergence rate. We also introduce a risk correction approach to mitigate overfitting problems, whose consistency and convergence rate are also proven.
  arXiv  Detail & Related papers  (2023-10-09T11:44:50Z)
• ALUM: Adversarial Data Uncertainty Modeling from Latent Model Uncertainty Compensation [25.67258563807856]
  We propose a novel method called ALUM to handle the model uncertainty and data uncertainty in a unified scheme. Our proposed ALUM is model-agnostic which can be easily implemented into any existing deep model with little extra overhead.
  arXiv  Detail & Related papers  (2023-03-29T17:24:12Z)
• Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval [51.83967175585896]
  UAL aims at providing reliability-aware predictions by considering data uncertainty and model uncertainty simultaneously. Data uncertainty captures the noise" inherent in the sample, while model uncertainty depicts the model's confidence in the sample's prediction.
  arXiv  Detail & Related papers  (2022-10-24T17:53:20Z)
• Estimating Model Performance under Domain Shifts with Class-Specific Confidence Scores [25.162667593654206]
  We introduce class-wise calibration within the framework of performance estimation for imbalanced datasets. We conduct experiments on four tasks and find the proposed modifications consistently improve the estimation accuracy for imbalanced datasets.
  arXiv  Detail & Related papers  (2022-07-20T15:04:32Z)
• Certifying Data-Bias Robustness in Linear Regression [12.00314910031517]
  We present a technique for certifying whether linear regression models are pointwise-robust to label bias in a training dataset. We show how to solve this problem exactly for individual test points, and provide an approximate but more scalable method. We also unearth gaps in bias-robustness, such as high levels of non-robustness for certain bias assumptions on some datasets.
  arXiv  Detail & Related papers  (2022-06-07T20:47:07Z)
• Assessing the Quality of the Datasets by Identifying Mislabeled Samples [14.881597737762316]
  We propose a novel statistic -- noise score -- as a measure for the quality of each data point to identify mislabeled samples. In our work, we use the representations derived by the inference network of data quality supervised variational autoencoder (AQUAVS) We validate our proposed statistic through experimentation by corrupting MNIST, FashionMNIST, and CIFAR10/100 datasets.
  arXiv  Detail & Related papers  (2021-09-10T17:14:09Z)
• Learning from Similarity-Confidence Data [94.94650350944377]
  We investigate a novel weakly supervised learning problem of learning from similarity-confidence (Sconf) data. We propose an unbiased estimator of the classification risk that can be calculated from only Sconf data and show that the estimation error bound achieves the optimal convergence rate.
  arXiv  Detail & Related papers  (2021-02-13T07:31:16Z)
• 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)
• How Training Data Impacts Performance in Learning-based Control [67.7875109298865]
  This paper derives an analytical relationship between the density of the training data and the control performance. We formulate a quality measure for the data set, which we refer to as $rho$-gap. We show how the $rho$-gap can be applied to a feedback linearizing control law.
  arXiv  Detail & Related papers  (2020-05-25T12:13:49Z)

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.