This paper presents SpeeLFC, a structured probabilistic model that incorporates the constraints of probability axioms for parameters of the crowd layer, which ...
End-to-end learning from crowds has recently been introduced as an EM-free approach to training deep neural networks directly from noisy crowdsourced.
Jan 7, 2021 · This paper presents SpeeLFC, a structured probabilistic model that incorporates the constraints of probability axioms for parameters of the ...
This paper presents SpeeLFC, a structured probabilistic model that incorporates the constraints of probability axioms for parameters of the crowd layer, which ...
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<jats:p>End-to-end learning from crowds has recently been introduced as an EM-free approach to training deep neural networks directly from noisy ...
A novel general-purpose crowd layer is proposed, which allows us to train deep neural networks end-to-end, directly from the noisy labels of multiple ...
Learning from Crowds (LFC) seeks to induce a high-quality classifier from training instances, which are linked to a range of possible noisy annotations from ...
This work studies the novel Crowdsourced Multi-. Label Learning (CMLL) problem, where each instance is related to multiple true labels but the.
Jun 29, 2021 · [2018] propose the Crowd Layer to train deep neural networks end-to-end directly from the noisy crowdsourced labels, using only back-propagation ...
In this study, we introduce an innovative end-to-end MIL model that concurrently trains the CNN backbone and attention mechanism along with the GP classifier.