Research Article
Learn to Rectify Label Through Kernel Extreme Learning Machine
@INPROCEEDINGS{10.1007/978-3-030-77569-8_19, author={Qiang Cai and Fenghai Li and Haisheng Li and Jian Cao and Shanshan Li}, title={Learn to Rectify Label Through Kernel Extreme Learning Machine}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 16th EAI International Conference, QShine 2020, Virtual Event, November 29--30, 2020, Proceedings}, proceedings_a={QSHINE}, year={2021}, month={6}, keywords={Convolutional Neural Networks Kernel extreme learning machine Image classification}, doi={10.1007/978-3-030-77569-8_19} }- Qiang Cai
Fenghai Li
Haisheng Li
Jian Cao
Shanshan Li
Year: 2021
Learn to Rectify Label Through Kernel Extreme Learning Machine
QSHINE
Springer
DOI: 10.1007/978-3-030-77569-8_19
Abstract
Recent studies attempt to construct complicated and redundant Convolutional Neural Networks (CNNs) to improve image classification performance. In this paper, instead of painstakingly designing a CNN’s architecture, we consider promoting classification performance by revising CNN’s classification results. We therefore propose a novel image classification approach that Learns to Rectify Label (LRL) through Kernel Extreme Learning Machine (KELM). It includes two phases: (1) Pre classification, we put images into a trained CNN to generate corresponding incomplete labels. (2) Label Rectification, the incomplete labels are rectified by the KELM’s high-dimensional mapping, so final classification results are acquired. Extensive experiments conducted on public datasets demonstrate the effectiveness of our method. At the meantime, our method has well generalizability that can be integrated with many popular networks.
