Self-supervised methods based on contrastive learning have achieved great
success in unsupervised visual representation learning. However, most methods
under this framework suffer from the problem of false negative samples.
Inspired by mean shift for self-supervised learning, we propose a new simple
framework, namely Multiple Sample Views and Queues (MSVQ). We jointly construct
a soft label on-the-fly by introducing two complementary and symmetric ways:
multiple augmented positive views and two momentum encoders forming various
semantic features of negative samples. Two teacher networks perform similarity
relationship calculations with negative samples and then transfer this
knowledge to the student. Let the student mimic the similar relationship
between the samples, thus giving the student a more flexible ability to
identify false negative samples in the dataset. The classification results on
four benchmark image datasets demonstrate the high effectiveness and efficiency
of our approach compared to some classical methods. Source code and pretrained
models are available at $\href{this https URL}{this~http~URL}$.

本文介绍了一种基于 Contrastive Learning 并受均值漂移启发的自监督学习框架 MSVQ，该框架通过引入多个正样本视角和两个负样本动量编码器形成各种语义特征，最终通过两个教师网络对学生进行知识传输并提高样本分类和识别的能力。该方法在四个基准图像数据集上取得了高效和高效的分类结果。