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Global-and-Local sampling for efficient hybrid task self-supervised learning.
- Source :
-
Knowledge-Based Systems . May2023, Vol. 268, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- Siamese-architecture-based self-supervised learning usually suffers from relatively high computational consumption and unsatisfactory performance because of its slow convergence and poor feature extraction capability. To alleviate these issues, we propose a self-supervised method, called SSL 2 , that is both efficient and effective. Specifically, a global and local sampling method is proposed to increase the number of samples while maintaining connections between semantic features. More significantly, SSL 2 maintains low computational complexity and facilitates the establishment of mapping relationships between global comprehensive and local detailed features compared with other methods. In addition, an i nformation r etainer p rojection h ead (IRPH) is proposed to further balance the information between detailed inconsistency and semantic consistency. Finally, hybrid tasks are embedded into SSL 2 to optimize the model so that it can effectively leverage the data provided by global and local sampling. Extensive qualitative and quantitative evaluations of various types of benchmarks illustrate that SSL 2 outperforms existing self-supervised frameworks in commonly used computer vision tasks. Specifically, SSL 2 achieved satisfactory performance with linear classification on ImageNet, outperforming MoCo-v2 by 2.2% with fewer calculations, and it also achieved competitive results compared with other state-of-the-art methods. • A new single-branch self-supervised learning framework is designed. • A novel sampling strategy is designed to make full use of the information. • A head is designed to balance detailed inconsistency and semantic consistency. • Well-balanced hybrid loss functions are introduced. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SUPERVISED learning
*COMPUTER vision
*FEATURE extraction
*COMPUTATIONAL complexity
Subjects
Details
- Language :
- English
- ISSN :
- 09507051
- Volume :
- 268
- Database :
- Academic Search Index
- Journal :
- Knowledge-Based Systems
- Publication Type :
- Academic Journal
- Accession number :
- 163001795
- Full Text :
- https://doi.org/10.1016/j.knosys.2023.110479