The semantic-preserving regularizer is then defined as: By penalizing L sp , the learned transformation function g is explicitly encouraged to retain the semantic similarity information of the original continuous embeddings.
the entire objective function to be optimized is: where λ sp controls the relative weight between the reconstruction loss (L rec ) and semantic-preserving loss (L sp ).