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A neural network layer that enables training of deep neural networks directly from crowdsourced labels (e.g. from Amazon Mechanical Turk) or, more generally, labels from multiple annotators with different biases and levels of expertise.

Home Page: http://www.fprodrigues.com/publications/deep-crowds/

License: GNU General Public License v3.0

Jupyter Notebook 94.71% Perl 1.86% Python 3.43%
deep-learning crowdsourcing keras tensorflow multiple-annotators deep-neural-networks image-classification crowd text-regression sequence-labeling

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crowdlayer's Issues

Is it necessary to use the augmented Labelme features provided by you?

I tried to train from the original images and annotations downloaded from your homepage but obtain bad results using crowd layer and doctor net. But I achieved similar results using Majority-vote and DS labels. So I wonder if crowd layer still works when training from a dataset whose most samples lack annotations especially when there are so many annotators but most of them did not label all samples.

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