Open this publication in new window or tab >>Show others...
2024 (English)In: Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LIV / [ed] Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol, Springer Nature Switzerland , 2024, Vol. 15112, p. 307-327Conference paper, Published paper (Refereed)
Abstract [en]
Long-tailed semi-supervised learning (LTSSL) represents a practical scenario for semi-supervised applications, challenged by skewed labeled distributions that bias classifiers. This problem is often aggravated by discrepancies between labeled and unlabeled class distributions, leading to biased pseudo-labels, neglect of rare classes, and poorly calibrated probabilities. To address these issues, we introduce Flexible Distribution Alignment (FlexDA), a novel adaptive logit-adjusted loss framework designed to dynamically estimate and align predictions with the actual distribution of unlabeled data and achieve a balanced classifier by the end of training. FlexDA is further enhanced by a distillation-based consistency loss, promoting fair data usage across classes and effectively leveraging underconfident samples. This method, encapsulated in ADELLO (Align and Distill Everything All at Once), proves robust against label shift, significantly improves model calibration in LTSSL contexts, and surpasses previous state-of-of-art approaches across multiple benchmarks, including CIFAR100-LT, STL10-LT, and ImageNet127, addressing class imbalance challenges in semi-supervised learning. Our code is available at https://github.com/emasa/ADELLO-LTSSL.
Place, publisher, year, edition, pages
Springer Nature Switzerland, 2024
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 15112
National Category
Computer Systems
Identifiers
urn:nbn:se:liu:diva-209223 (URN)10.1007/978-3-031-72949-2_18 (DOI)001352860600018 ()2-s2.0-85208545165 (Scopus ID)9783031729485 (ISBN)9783031729492 (ISBN)
Conference
18th European Conference, Milan, Italy, September 29–October 4, 2024
Note
Funding Agencies|Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation; Swedish Research Council [2022-06725]; Knut and Alice Wallenberg Foundation at the National Supercomputer Centre
2024-11-062024-11-062024-12-17