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8804f94e16ba5b680e239a554a08f7d2-AuthorFeedback.pdf
We train the autoencoder and the classifier on the training set, which is6 diverse and contains texts of varying degrees of attributes, reflected by the different confidence values given by the7 classifier. Different from most previous work that only provides binary control overattributes, one advantage of our model is13 the ability to givecontrol over the degree of attribute transfer desired. Particularly, 'Acc' is used to evaluate the attribute's accuracy.
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.43)