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Would you please tell me about how to train the boundaries to manipulate multiple semantics?
is it putting two trained boundaries together or computing the covariance of diferent semantics' scores?
As far as I understand, for each of the semantics, there is going to be different SVM linear classifier and the corresponding normal. Then as the paper suggests in section 2.2, you perform n1-(n1^T n2)n2 to obtain a new normal that you can add on to the original latent code to manipulate semantic n1 without affecting n2.
So in short, train 2 independent SVM classifiers with different semantic attribute scores, and the semantic scores is going to be a scalar, for each of them.
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songwoh commentedon Nov 26, 2021
As far as I understand, for each of the semantics, there is going to be different SVM linear classifier and the corresponding normal. Then as the paper suggests in section 2.2, you perform n1-(n1^T n2)n2 to obtain a new normal that you can add on to the original latent code to manipulate semantic n1 without affecting n2.
So in short, train 2 independent SVM classifiers with different semantic attribute scores, and the semantic scores is going to be a scalar, for each of them.