基于对比自监督学习的图像分类框架

Image classification framework based on contrastive self-supervised learning

为解决监督学习在图像分类领域中需要耗费大量时间来完成数据集标注等问题,提出了一种自监督学习图像分类框架:SSIC(Self-supervised image classification)框架。SSIC框架是一种基于对比学习的自监督学习方法,比现有的无监督方法有更好的性能。设计了一种新的框架结构,并选择了更有效的代理任务来提高模型的鲁棒性。此外,提出了有针对性的损失函数来提升图像分类性能。模型在UC-Merced、NWPU、AID三个公开的数据集上进行实验,实验结果表明:SSIC框架与当前最新技术相比有明显的优势,并且在低分辨率图像分类中也表现出色。

In order to solve the problem that supervised learning needs a lot of time to complete data set annotation in the field of image classification,a self-supervised image classification framework,SSIC framework,is proposed. SSIC framework is a self supervised learning method based on contrastive learning,which has better performance than the existing unsupervised methods. A new framework is designed and a more effective pretext task is selected to improve the robustness of the model. In addition,a targeted loss function is proposed to improve the performance of image classification. experiments was conducted on UC Merced,NWPU and AID data sets. Experimental results show that SSIC framework has obvious advantages over the latest technology,and it also performs well in low resolution image classification.