Privileged Semi-Supervised Learning
Xingyu Chen, Chen Gong, Chao Ma, Xiaolin Huang, Jie Yang · 2018
Semi-Supervised Learning (SSL) aims to leverage unlabeled data to improve performance. Due to the lack of supervised information, previous works mainly focus on how to utilize the available unlabeled data to improve the training quality. However, the estimation of the data distribution revealed by the unlabeled examples might not be accurate as their classes are unknown. Inspired by the framework of Learning Using Privileged Information (LUPI), we propose to introduce an intelligent “teacher” which can utilize the privileged information (i.e. more precise explanations of training set) to improve the performance of SSL. This developed method is named as Privileged Semi-Supervised Learning (PSSL). Moreover, our method can be efficiently solved with l2-loss. Experimentally, we compare our PSSL with several typical algorithms on three real-world datasets, and the results suggest that our approach is able to achieve the state-of-the-art performance.