2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIVMSP-8.2
Paper Title Robust Binary Loss for Multi-category Classification with Label Noise
Authors Defu Liu, Guowu Yang, University of Electronic Science and Technology of China, China; Jinzhao Wu, Guangxi University, China; Jiayi Zhao, Fengmao Lv, Southwestern University of Finance and Economics, China
SessionIVMSP-8: Machine Learning for Image Processing II
LocationGather.Town
Session Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVTEC] Image & Video Processing Techniques
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Deep learning has achieved tremendous success in image classification. However, the corresponding performance leap relies heavily on large-scale accurate annotations, which are usually hard to collect in reality. It is essential to explore methods that can train deep models effectively under label noise. To address the problem, we propose to train deep models with robust binary loss functions. To be specific, we tackle the $K$-class classification task by using $K$ binary classifiers. We can immediately use multi-category large margin classification approaches, e.g., Pairwise-Comparison (PC) or One-Versus-All (OVA), to jointly train the binary classifiers for multi-category classification. Our method can be robust to label noise if symmetric functions, e.g., the sigmoid loss or the ramp loss, are employed as the binary loss function in the framework of risk minimization. The learning theory reveals that our method can be inherently tolerant to label noise in multi-category classification tasks. Extensive experiments over different datasets with different types of label noise are conducted. The experimental results clearly confirm the effectiveness of our method.