Regularly Truncated M-Estimators for Learning With Noisy Labels

The sample selection approach is very popular in learning with noisy labels. As deep networks "learn pattern first" , prior methods built on sample selection share a similar training procedure: the small-loss examples can be regarded as clean examples and used for helping generalization, w...

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Vydáno v:	IEEE transactions on pattern analysis and machine intelligence Ročník 46; číslo 5; s. 3522 - 3536
Hlavní autoři:	Xia, Xiaobo, Lu, Pengqian, Gong, Chen, Han, Bo, Yu, Jun, Liu, Tongliang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.05.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Australia Computer science Estimators generalization Labels Learning Learning with noisy labels Noise measurement Random variables regularly truncated M-estimators sample selection Switches Training Training data truncated M-estimators
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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