The Over-Certainty Phenomenon in Modern UDA Algorithms

• URL: http://arxiv.org/abs/2404.16168v2
• Date: Mon, 27 May 2024 23:58:00 GMT
• Title: The Over-Certainty Phenomenon in Modern UDA Algorithms
• Authors: Fin Amin, Jung-Eun Kim,
• Abstract summary: We aim to recalibrate a neural network's decision boundaries in relation to its cognizance of the data it observes. We propose a UDA algorithm that not only augments accuracy but also assures model calibration.
• Score: 3.301728339780329
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: When neural networks are confronted with unfamiliar data that deviate from their training set, this signifies a domain shift. While these networks output predictions on their inputs, they typically fail to account for their level of familiarity with these novel observations. This challenge becomes even more pronounced in resource-constrained settings, such as embedded systems or edge devices. To address such challenges, we aim to recalibrate a neural network's decision boundaries in relation to its cognizance of the data it observes, introducing an approach we coin as certainty distillation. While prevailing works navigate unsupervised domain adaptation (UDA) with the goal of curtailing model entropy, they unintentionally birth models that grapple with calibration inaccuracies - a dilemma we term the over-certainty phenomenon. In this paper, we probe the drawbacks of this traditional learning model. As a solution to the issue, we propose a UDA algorithm that not only augments accuracy but also assures model calibration, all while maintaining suitability for environments with limited computational resources.

Related papers

• Uncertainty-Guided Alignment for Unsupervised Domain Adaptation in Regression [5.437298646956505]
  Unsupervised Domain Adaptation for Regression (UDAR) aims to adapt models from a labeled source domain to an unlabeled target domain for regression tasks. Traditional feature alignment methods, successful in classification, often prove ineffective for regression due to the correlated nature of regression features. We propose Uncertainty-Guided Alignment (UGA), a novel method that integrates predictive uncertainty into the feature alignment process.
  arXiv  Detail & Related papers  (2024-01-24T14:55:02Z)
• Deep Neural Networks Tend To Extrapolate Predictably [51.303814412294514]
  neural network predictions tend to be unpredictable and overconfident when faced with out-of-distribution (OOD) inputs. We observe that neural network predictions often tend towards a constant value as input data becomes increasingly OOD. We show how one can leverage our insights in practice to enable risk-sensitive decision-making in the presence of OOD inputs.
  arXiv  Detail & Related papers  (2023-10-02T03:25:32Z)
• 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)
• CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars [11.479857808195774]
  We propose a Continual Unsupervised Domain Adaptation (CONDA) approach that allows the model to continuously learn and adapt with respect to the presence of the new data. To avoid the catastrophic forgetting problem and maintain the performance of the segmentation models, we present a novel Bijective Maximum Likelihood loss.
  arXiv  Detail & Related papers  (2022-12-01T16:15:54Z)
• Single Model Uncertainty Estimation via Stochastic Data Centering [39.71621297447397]
  We are interested in estimating the uncertainties of deep neural networks. We present a striking new finding that an ensemble of neural networks with the same weight initialization, trained on datasets that are shifted by a constant bias gives rise to slightly inconsistent trained models. We show that $Delta-$UQ's uncertainty estimates are superior to many of the current methods on a variety of benchmarks.
  arXiv  Detail & Related papers  (2022-07-14T23:54:54Z)
• A heteroencoder architecture for prediction of failure locations in porous metals using variational inference [1.2722697496405462]
  We employ an encoder-decoder convolutional neural network to predict the failure locations of porous metal tension specimens. The objective of predicting failure locations presents an extreme case of class imbalance since most of the material in the specimens do not fail. We demonstrate that the resulting predicted variances are effective in ranking the locations that are most likely to fail in any given specimen.
  arXiv  Detail & Related papers  (2022-01-31T20:26:53Z)
• Training on Test Data with Bayesian Adaptation for Covariate Shift [96.3250517412545]
  Deep neural networks often make inaccurate predictions with unreliable uncertainty estimates. We derive a Bayesian model that provides for a well-defined relationship between unlabeled inputs under distributional shift and model parameters. We show that our method improves both accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2021-09-27T01:09:08Z)
• 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)
• Attribute-Guided Adversarial Training for Robustness to Natural Perturbations [64.35805267250682]
  We propose an adversarial training approach which learns to generate new samples so as to maximize exposure of the classifier to the attributes-space. Our approach enables deep neural networks to be robust against a wide range of naturally occurring perturbations.
  arXiv  Detail & Related papers  (2020-12-03T10:17:30Z)
• 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)
• Uncertainty-Aware Deep Classifiers using Generative Models [7.486679152591502]
  Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. We develop a novel neural network model that is able to express both aleatoric and epistemic uncertainty to distinguish decision boundary and out-of-distribution regions.
  arXiv  Detail & Related papers  (2020-06-07T15:38:35Z)

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.