Supervised Feature Learning via Within-Class Reconstruction
Yunxue Shao, Jiantao Zhou, Guanglai Gao · 2017
Feature representation of data is a key issue for recognition related tasks. Inspired by the creative ability of human beings, in this paper we propose a novel feature learning framework named within-class reconstruction (WCR). In WCR, the feature representation of the input sample are used to reconstruct all the samples within the same class. We minimize the mean squared error (MSE) cost function to update feature extracting functions. Furthermore, most unsupervised learning methods such as auto-encoders could embed in the proposed framework. To evaluate the effectiveness of the proposed framework, CNN is used to extract the feature representations and reconstruct the within-class samples. The experimental results demonstrate that the representations learned by the proposed WCR achieve better performance than that of auto-encoders. All the codes have been made publicly available at https://github.com/step123456789/wcr.