Timezone: »

Confidence-based Reliable Learning under Dual Noises
Peng Cui · Yang Yue · Zhijie Deng · Jun Zhu


Deep neural networks (DNNs) have achieved remarkable success in a variety of computer vision tasks, where massive labeled images are routinely required for model optimization. Yet, the data collected from the open world are unavoidably polluted by noise, which may significantly undermine the efficacy of the learned models. Various attempts have been made to reliably train DNNs under data noise, but they separately account for either the noise existing in the labels or that existing in the images. A naive combination of the two lines of works would suffer from the limitations in both sides, and miss the opportunities to handle the two kinds of noise in parallel. This works provides a first, unified framework for reliable learning under the joint (image, label)-noise. Technically, we develop a confidence-based sample filter to progressively filter out noisy data without the need of pre-specifying noise ratio. Then, we penalize the model uncertainty of the detected noisy data instead of letting the model continue over-fitting the misleading information in them. Experimental results on various challenging synthetic and real-world noisy datasets verify that the proposed method can outperform competing baselines in the aspect of classification performance.

Author Information

Peng Cui (Tsinghua university)
Yang Yue (Tsinghua University, Tsinghua University)
Zhijie Deng (Shanghai Jiao Tong University)
Zhijie Deng

Zhijie Deng joined Qing Yuan Research Institute of Shanghai Jiao Tong University as a tenure-track assistant professor in July 2022. He obtained the Ph.D. degree from Department of Computer Science and Technology, Tsinghua University in June 2022, under the supervision of Prof. Bo Zhang and Prof. Jun Zhu.

Jun Zhu (Tsinghua University)

More from the Same Authors