In this paper, we propose a novel image process scheme called class-based expansion learning for image classification, which aims at improving the supervision-stimulation frequency for the samples of the confusing classes. Class-based expansion learning takes a bottom-up growing strategy in a class-based expansion optimization fashion, which pays more attention to the quality of learning the fine-grained classification boundaries for the preferentially selected classes. Besides, we develop a class confusion criterion to select the confusing class preferentially for training. In this way, the classification boundaries of the confusing classes are frequently stimulated, resulting in a fine-grained form. Experimental results demonstrate the effectiveness of the proposed scheme on several benchmarks.

翻译：在本文中,我们提出一个新的图像过程计划,名为“基于阶级的扩展学习”,用于图像分类,目的是改善对各类混杂样本的监督刺激频率。基于阶级的扩展学习以基于阶级的扩展优化方式采取自下而上的增长战略,更加关注学习优选班级细微分类界限的质量。此外,我们还制定了一个阶级混乱标准,以优先选择混乱的班级进行培训。这样,混淆班级的分类界限就经常受到刺激,从而形成细微的分类形式。实验结果表明所提议的计划在若干基准上的有效性。