mlsanityData to 7 Oct 2026

K-means Clustering Based Feature Consistency Alignment for Label-free Model Evaluation

arXiv 2304.09758 ↗First posted 17 Apr 2023cs.LG cs.CVcited by 4

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Abstract (arXiv)

The label-free model evaluation aims to predict the model performance on various test sets without relying on ground truths. The main challenge of this task is the absence of labels in the test data, unlike in classical supervised model evaluation. This paper presents our solutions for the 1st DataCV Challenge of the Visual Dataset Understanding workshop at CVPR 2023. Firstly, we propose a novel method called K-means Clustering Based Feature Consistency Alignment (KCFCA), which is tailored to handle the distribution shifts of various datasets. KCFCA utilizes the K-means algorithm to cluster labeled training sets and unlabeled test sets, and then aligns the cluster centers with feature consistency. Secondly, we develop a dynamic regression model to capture the relationship between the shifts in distribution and model accuracy. Thirdly, we design an algorithm to discover the outlier model factors, eliminate the outlier models, and combine the strengths of multiple autoeval models. On the DataCV Challenge leaderboard, our approach secured 2nd place with an RMSE of 6.8526. Our method significantly improved over the best baseline method by 36% (6.8526 vs. 10.7378). Furthermore, our method achieves a relatively more robust and optimal single model performance on the validation dataset.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 29 of its 52 references that are in the map. First posted on arXiv 17 Apr 2023.

Builds on

29 of 52 references.

References · 29

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2016Densely Connected Convolutional Networkscited by 6,691
  4. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,857
  5. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  6. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
  7. 2020Energy-based Out-of-distribution Detectioncited by 1,296
  8. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,274
  9. 2017Selective Classification for Deep Neural Networkscited by 799
  10. 2017Exploring Generalization in Deep Learningcited by 595
  11. 2021RepVGG: Making VGG-style ConvNets Great Againcited by 520
  12. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
17 more
  1. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 451
  2. 2018Stronger generalization bounds for deep nets via a compression approachcited by 306
  3. 2018Learning Confidence for Out-of-Distribution Detection in Neural Networkscited by 245
  4. 2018Do CIFAR-10 Classifiers Generalize to CIFAR-10?cited by 197
  5. 2022Leveraging Unlabeled Data to Predict Out-of-Distribution Performancecited by 141
  6. 2021Assessing Generalization of SGD via Disagreementcited by 116
  7. 2021Predicting with Confidence on Unseen Distributionscited by 116
  8. 2018Predicting the Generalization Gap in Deep Networks with Margin Distributionscited by 111
  9. 2020Rethinking Bias-Variance Trade-off for Generalization of Neural Networkscited by 107
  10. 2020Are Labels Always Necessary for Classifier Accuracy Evaluation?cited by 104
  11. 2018Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networkscited by 89
  12. 2021What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?cited by 63
  13. 2021Mandoline: Model Evaluation under Distribution Shiftcited by 55
  14. 2020Computing the Testing Error without a Testing Setcited by 54
  15. 2022Predicting Out-of-Distribution Error with the Projection Normcited by 50
  16. 2020More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Modelscited by 27
  17. 2021Predicting Deep Neural Network Generalization with Perturbation Response Curvescited by 14

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Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2024AcTracer: Active Testing of Large Language Model via Multi-Stage Samplingcited by 16
  2. 2025Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settingscited by 4
  3. 2024Poor-Supervised Evaluation for SuperLLM via Mutual Consistencycited by 1
  4. 2023Are Easy Data Easy (for K-Means)cited by 0