Unsupervised embedding learning based on neighbors information

Abstract

Although the convenience of Deep learning in many areas lacking annotation is still a significant drawback for training the model However unsupervised embedding learning can be regarded as a helper for the pre-training task even we do not have the information of labels A visually meaningful embedding must satisfy the properties closing the similar instances and separating those dissimilar We proposed two approaches based on neighbor information in this paper super-AND and NB-DSCV Considering neighbor information is critical in the unsupervised embedding learning task Therefore we leverage the neighbor's information to update the embedding by exploitation and exploration We get a considerable improvement on unsupervised embedding learning tasks on our experiment result In future work we want to implement these approaches from visual data to the natural language process problem

Date of Award	2020
Original language	English
Supervisor	Cheng-Te Li (Supervisor)