A Comprehensive Approach to Unsupervised Embedding Learning based on AND Algorithm

• URL: http://arxiv.org/abs/2002.12158v1
• Date: Wed, 26 Feb 2020 13:22:04 GMT
• Title: A Comprehensive Approach to Unsupervised Embedding Learning based on AND Algorithm
• Authors: Sungwon Han, Yizhan Xu, Sungwon Park, Meeyoung Cha, Cheng-Te Li
• Abstract summary: Unsupervised embedding learning aims to extract good representation from data without the need for any manual labels. This paper proposes a new unsupervised embedding approach, called Super-AND, which extends the current state-of-the-art model. Super-AND outperforms all existing approaches and achieves an accuracy of 89.2% on the image classification task for CIFAR-10.
• Score: 18.670975246545208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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