Deep Learning From Multiple Noisy Annotators as A Union.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3168696
Abstract: Crowdsourcing is a popular solution for large-scale data annotations. So far, various end-to-end deep learning methods have been proposed to improve the practical performance of learning from crowds. Despite their practical effectiveness, most of them… read more here.
Keywords: annotators union; learning multiple; multiple noisy; deep learning ... See more keywords
Towards Accurate and Robust Domain Adaptation Under Multiple Noisy Environments
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2022.3215150
Abstract: In many non-stationary environments, machine learning algorithms usually confront the distribution shift scenarios. Previous domain adaptation methods have achieved great success. However, they would lose algorithm robustness in multiple noisy environments where the examples of… read more here.
Keywords: domain adaptation; multiple noisy; robust domain; domain ... See more keywords