In contrastive self-supervised learning, positive samples are typically drawn
from the same image but in different augmented views, resulting in a relatively
limited source of positive samples. An effective way to alleviate this problem
is to incorporate the relationship between samples, which involves including
the top-k nearest neighbors of positive samples in the framework. However, the
problem of false neighbors (i.e., neighbors that do not belong to the same
category as the positive sample) is an objective but often overlooked challenge
due to the query of neighbor samples without human supervision. In this paper,
we present a simple Self-supervised learning framework called Mixed
Nearest-Neighbors for Self-Supervised Learning (MNN). MNN optimizes the
influence of neighbor samples on the semantics of positive samples through an
intuitive weighting approach and image mixture operations. The results of our
study demonstrate that MNN exhibits exceptional generalization performance and
training efficiency on four benchmark datasets.

通过引入最近邻样本和图像混合操作，该研究提出了一种名为 MNN 的简单自监督学习框架，优化了最近邻样本对正样本语义的影响，展示了在四个基准数据集上出色的泛化性能和训练效率。