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Improving consensus extraction from noisy labels is a very popular topic, the main focus being binary label data.
This means that the labeled diagnoses are not perfectly reliable, and techniques for supervised learning from noisy labels must be incorporated.
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The Dawid-Skene estimator has been widely used for inferring the true labels from the noisy labels provided by non-expert crowdsourcing workers.
Co-teaching: Robust training of deep neural networks with noisy labels.
Reed, S. et al. Training deep neural networks on noisy labels with bootstrapping.
This necessitates the use of methods that are robust to noisy labels [ 77, 78].
Most predictive modeling-based explorations of connectomes have utilized classification methods that are sensitive to noisy labels.
Then, the noisy label proportion is p k =p k +n k, where n k ∼ N μ,σ 2).
The flowchart of our algorithm is graphically shown in Figure 7. Notice that since the number of labels and CC were recovered from noisy data, they may differ from the real set of panels.
... .. estimation from, noisy biophysical observations.
We develop algorithms for reconstructing particles from noisy diffraction patterns.
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