A1 Refereed original research article in a scientific journal
Test-Time Learning for Outlier Detection
Authors: Yang, Jiawei; Chen, Jingdong; Rahardja, Susanto
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publication year: 2026
Journal: IEEE Transactions on Knowledge and Data Engineering
ISSN: 1041-4347
eISSN: 2326-3865
DOI: https://doi.org/10.1109/TKDE.2026.3689274
Publication's open availability at the time of reporting: No Open Access
Publication channel's open availability : Partially Open Access publication channel
Web address : https://doi.org/10.1109/tkde.2026.3689274
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/526499973
Self-archived copy's licence: All rights reserved
Self-archived copy's version: Final draft
In this work, the concept of test-time learning is presented, wherein Machine-Learning (ML) models are constructed by involving unlabeled test samples. Based on this concept, we propose a method called Local Augment (LA) designed to improve the performance of trained outlier detectors at the prediction stage without altering the trained models or accessing the training data. LA operates under the only assumption that the model should produce similar outputs for similar inputs, implying that the prediction of a given sample can be enhanced by the predictions for its similar samples. Specifically, LA boosts outlier detection performance during prediction by fusing the outlier score of a given sample with the scores of synthetically neighboring samples generated by adding random perturbations to the given sample. This simple method demonstrates an average improvement of about +0.04 Area Under the Receiver Operating Characteristic curve (AUROC) across 26 real-world datasets for all 14 tested detectors. Notably, this represents the pioneering work of enhancing ML models during the prediction stage without the need to modify the trained models or access the training dataset. This work opens up new possibilities for addressing existing bottleneck problems in various ML tasks beyond outlier detection in diverse domains.
Keywords:
LA, local augment, outlier detection, test-time learning
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Funding information in the publication:
This work was supported in part by the National Key Research and Development Program of China, STI 2030 Major Projects under Grant 2021ZD0201502. This project has received funding from the European Union’s Horizon Europe research and innovation programme under Marie Sklodowska-Curie grant agreement no [101126611].
