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• W2883672997 abstract "Label aggregation is an efficient and low cost way to make large datasets for supervised learning. It takes the noisy labels provided by non-experts and infers the unknown true labels. In this paper, we propose a novel label aggregation algorithm which includes a label aggregation neural network. The learning task in this paper is unsupervised. In order to train the neural network, we try to design a suitable guiding model to define the loss function. The optimization goal of our algorithm is to find the consensus between the predictions of the neural network and the guiding model. This algorithm is easy to optimize using mini-batch stochastic optimization methods. Since the choices of the neural network and the guiding model are very flexible, our label aggregation algorithm is easy to extend. According to the algorithm framework, we design two novel models to aggregate noisy labels. Experimental results show that our models achieve better results than state-of-the-art label aggregation methods." @default.
• W2883672997 created "2018-08-03" @default.
• W2883672997 creator A5000610648 @default.
• W2883672997 creator A5070094794 @default.
• W2883672997 date "2018-07-19" @default.
• W2883672997 modified "2023-09-24" @default.
• W2883672997 title "Label Aggregation via Finding Consensus Between Models" @default.
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• W2883672997 hasPublicationYear "2018" @default.
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