Color-S⁴L: Self-supervised Semi-supervised Learning with Image Colorization

2024/01/08 by Hanxiao Chen, Chen, Hanxiao · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Cancer-related molecular mechanisms research #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning and Data Classification

paper · pdf · doi:10.48550/arxiv.2401.03753

openalex publication_date 2024/01/08 · openalex created_date 2024/01/13 · openalex updated_date 2026/07/28

Abstract

This work addresses the problem of semi-supervised image classification tasks with the integration of several effective self-supervised pretext tasks. Different from widely-used consistency regularization within semi-supervised learning, we explored a novel self-supervised semi-supervised learning framework (Color-S⁴L) especially with image colorization proxy task and deeply evaluate performances of various network architectures in such special pipeline. Also, we demonstrated its effectiveness and optimal performance on CIFAR-10, SVHN and CIFAR-100 datasets in comparison to previous supervised and semi-supervised optimal methods.

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