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Ryota Higashimoto, Soh Yoshida and Mitsuji Muneyasu
This paper addresses the performance degradation of deep neural networks caused by learning with noisy labels. Recent research on this topic has exploited the memorization effect: networks fit data with clean labels during the early stages of learning an...
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Ziyang Wang and Irina Voiculescu
Conventional deep learning methods have shown promising results in the medical domain when trained on accurate ground truth data. Pragmatically, due to constraints like lack of time or annotator inexperience, the ground truth data obtained from clinical ...
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Songyuan Li, Hao Zeng, Huanyu Wang and Xi Li
Salient Object Detection (SOD) aims at identifying the most visually distinctive objects in a scene. However, learning a mapping directly from a raw image to its corresponding saliency map is still challenging. First, the binary annotations of SOD impede...
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HaeHwan Kim, Ho-Woong Lee, JinSung Lee, Okhwan Bae and Chung-Pyo Hong
Detecting and tracking objects of interest in videos is a technology that can be used in various applications. For example, identifying cell movements or mutations through videos obtained in real time can be useful information for decision making in the ...
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Jialin Shi, Chenyi Guo and Ji Wu
Deep-learning models require large amounts of accurately labeled data. However, for medical image segmentation, high-quality labels rely on expert experience, and less-experienced operators provide noisy labels. How one might mitigate the negative effect...
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Yaojie Zhang, Huahu Xu, Junsheng Xiao and Minjie Bian
The real world is full of noisy labels that lead neural networks to perform poorly because deep neural networks (DNNs) are prone to overfitting label noise. Noise label training is a challenging problem relating to weakly supervised learning. The most ad...
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