  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 the mean shift for self-supervised learning, we propose a new
simple framework, namely Multiple Sample Views and Queues (MSVQ). We jointly
construct three soft labels on-the-fly by utilizing two complementary and
symmetric approaches: multiple augmented positive views and two momentum
encoders that generate various semantic features for negative samples. Two
teacher networks perform similarity relationship calculations with negative
samples and then transfer this knowledge to the student network. Let the
student network mimic the similarity relationships between the samples, thus
giving the student network 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 \href{https://github.com/pc-cp/MSVQ}{here}.
