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Active learning


Most current network models are trained by supervised bases. The cost of building these databases is high. Active learning consists in using a partially trained model to select the next set of data to annotate. These techniques allow to gain, on some applications, a ratio of 10 on the number of data to annotate to obtain the same model performance.

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