首页|Study Data from University of Electronic Science and Technology of China Update Understanding of Intelligent Systems (Separating Hard Clean Samples From Noisy S amples With Samples’ Learning Risk for Dnn When Learning With Noisy Labels)
Study Data from University of Electronic Science and Technology of China Update Understanding of Intelligent Systems (Separating Hard Clean Samples From Noisy S amples With Samples’ Learning Risk for Dnn When Learning With Noisy Labels)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning - Int elligent Systems is the subject of a report.According to news reporting out of Sichuan, People’s Republic of China, by NewsRx editors, researchstated, “Learni ng with Noisy Labels (LNL) methods aim to improve the accuracy of Deep Neural Networks (DNNs) when the training set contains samples with noisy or incorrect lab els, and have becomepopular in recent years. Existing popular LNL methods frequ ently regard samples with high learningdifficulty (high-loss and low prediction probability) as noisy samples; however, irregular feature patternsfrom hard cl ean samples can also cause high learning difficulty, which can lead to the miscl assification ofhard clean samples as noisy samples.”
SichuanPeople’s Republic of ChinaAsi aIntelligent SystemsMachine LearningUniversity of Electronic Science and T echnology of China
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