论文标题
Tribyol：三胞胎BYOL用于自我监督的代表学习
TriBYOL: Triplet BYOL for Self-Supervised Representation Learning
论文作者
论文摘要
本文提出了一种新颖的自我监督学习方法，用于学习具有小批量大小的更好表示。许多基于某些形式的暹罗网络的自我监督学习方法已经出现并受到了极大的关注。但是，这些方法需要使用大批量的大小来学习良好的表示并需要大量的计算资源。我们提出了一个新的三胞胎网络，并结合了三倍的损失，以提高小批量大小的自我监督表示学习的表现。实验结果表明，在小批处理情况下，我们的方法可以大大优于几个数据集上的最先进的自我监督学习方法。我们的方法为使用小批量大小的现实世界高分辨率图像提供了一种可行的解决方案，用于自我监督学习。
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.
