This paper proposes a novel self-supervised learning method for learning
better representations with small batch sizes. Many self-supervised learning
methods based on certain forms of the siamese network have emerged and received
significant attention. However, these methods need to use large batch sizes to
learn good representations and require heavy computational resources. We
present a new triplet network combined with a triple-view loss to improve the
performance of self-supervised representation learning with small batch sizes.
Experimental results show that our method can drastically outperform
state-of-the-art self-supervised learning methods on several datasets in
small-batch cases. Our method provides a feasible solution for self-supervised
learning with real-world high-resolution images that uses small batch sizes.

该文提出了一种新的自监督学习方法，使用小批量更好地表示。通过三元组网络和三元视图损失的组合，提高了小批量自监督表示学习的性能，这种方法在多个数据集中都可以显著优于现有的自监督学习方法，为处理使用小批量高分辨率图像的实际问题提供了可行的解决方案。