论文标题
一种基于和算法的无监督嵌入学习的综合方法
A Comprehensive Approach to Unsupervised Embedding Learning based on AND Algorithm
论文作者
论文摘要
无监督的嵌入学习旨在从数据中提取良好的表示，而无需任何手动标签，这在许多监督的学习任务中都是一个关键的挑战。本文提出了一种新的无监督嵌入方法，称为Super-and，该方法扩展了当前的最新模型。超级和具有独特的损失集，可以在低密度空间内收集附近的类似样品，同时保持不变特征与数据增强的完整功能。超级和胜过所有现有方法，并且在CIFAR-10的图像分类任务上获得了89.2％的精度。我们讨论了这种方法在协助半监督任务方面的实际含义。
Unsupervised embedding learning aims to extract good representation from data without the need for any manual labels, which has been a critical challenge in many supervised learning tasks. This paper proposes a new unsupervised embedding approach, called Super-AND, which extends the current state-of-the-art model. Super-AND has its unique set of losses that can gather similar samples nearby within a low-density space while keeping invariant features intact against data augmentation. Super-AND outperforms all existing approaches and achieves an accuracy of 89.2% on the image classification task for CIFAR-10. We discuss the practical implications of this method in assisting semi-supervised tasks.
