Transfer Knowledge from Head to Tail: Uncertainty Calibration under Long-tailed Distribution

How to estimate the uncertainty of a given model is a crucial problem. Current calibration techniques treat different classes equally and thus implicitly assume that the distribution of training data is balanced, but ignore the fact that real-world data often follows a long-tailed distribution. In t...

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Vydané v:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 19978 - 19987
Hlavní autori:	Chen, Jiahao, Su, Bing
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.06.2023
Predmet:	Adaptation models continual Head low-shot meta or long-tail learning Tail Temperature distribution Training Training data Transfer Uncertainty
ISSN:	1063-6919
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