Source-Free Domain-Invariant Performance Prediction

• URL: http://arxiv.org/abs/2408.02209v2
• Date: Tue, 6 Aug 2024 11:46:39 GMT
• Title: Source-Free Domain-Invariant Performance Prediction
• Authors: Ekaterina Khramtsova, Mahsa Baktashmotlagh, Guido Zuccon, Xi Wang, Mathieu Salzmann,
• Abstract summary: We propose a source-free approach centred on uncertainty-based estimation, using a generative model for calibration in the absence of source data. Our experiments on benchmark object recognition datasets reveal that existing source-based methods fall short with limited source sample availability. Our approach significantly outperforms the current state-of-the-art source-free and source-based methods, affirming its effectiveness in domain-invariant performance estimation.
• Score: 68.39031800809553
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurately estimating model performance poses a significant challenge, particularly in scenarios where the source and target domains follow different data distributions. Most existing performance prediction methods heavily rely on the source data in their estimation process, limiting their applicability in a more realistic setting where only the trained model is accessible. The few methods that do not require source data exhibit considerably inferior performance. In this work, we propose a source-free approach centred on uncertainty-based estimation, using a generative model for calibration in the absence of source data. We establish connections between our approach for unsupervised calibration and temperature scaling. We then employ a gradient-based strategy to evaluate the correctness of the calibrated predictions. Our experiments on benchmark object recognition datasets reveal that existing source-based methods fall short with limited source sample availability. Furthermore, our approach significantly outperforms the current state-of-the-art source-free and source-based methods, affirming its effectiveness in domain-invariant performance estimation.

Related papers

• Unsupervised Accuracy Estimation of Deep Visual Models using Domain-Adaptive Adversarial Perturbation without Source Samples [1.1852406625172216]
  We propose a new framework to estimate model accuracy on unlabeled target data without access to source data. Our approach measures the disagreement rate between the source hypothesis and the target pseudo-labeling function. Our proposed source-free framework effectively addresses the challenging distribution shift scenarios and outperforms existing methods requiring source data and labels for training.
  arXiv  Detail & Related papers  (2023-07-19T15:33:11Z)
• Predicting Out-of-Distribution Error with Confidence Optimal Transport [17.564313038169434]
  We present a simple yet effective method to predict a model's performance on an unknown distribution without any addition annotation. We show that our method, Confidence Optimal Transport (COT), provides robust estimates of a model's performance on a target domain. Despite its simplicity, our method achieves state-of-the-art results on three benchmark datasets and outperforms existing methods by a large margin.
  arXiv  Detail & Related papers  (2023-02-10T02:27:13Z)
• Uncertainty-guided Source-free Domain Adaptation [77.3844160723014]
  Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model. We propose quantifying the uncertainty in the source model predictions and utilizing it to guide the target adaptation.
  arXiv  Detail & Related papers  (2022-08-16T08:03:30Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• Unsupervised Adaptation of Semantic Segmentation Models without Source Data [14.66682099621276]
  We consider the novel problem of unsupervised domain adaptation of source models, without access to the source data for semantic segmentation. We propose a self-training approach to extract the knowledge from the source model. Our framework is able to achieve significant performance gains compared to directly applying the source model on the target data.
  arXiv  Detail & Related papers  (2021-12-04T15:13:41Z)
• A Prototype-Oriented Framework for Unsupervised Domain Adaptation [52.25537670028037]
  We provide a memory and computation-efficient probabilistic framework to extract class prototypes and align the target features with them. We demonstrate the general applicability of our method on a wide range of scenarios, including single-source, multi-source, class-imbalance, and source-private domain adaptation.
  arXiv  Detail & Related papers  (2021-10-22T19:23:22Z)
• Unsupervised Multi-source Domain Adaptation Without Access to Source Data [58.551861130011886]
  Unsupervised Domain Adaptation (UDA) aims to learn a predictor model for an unlabeled domain by transferring knowledge from a separate labeled source domain. We propose a novel and efficient algorithm which automatically combines the source models with suitable weights in such a way that it performs at least as good as the best source model.
  arXiv  Detail & Related papers  (2021-04-05T10:45:12Z)
• 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.