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We present a conditional generative model that maps low-dimensionalembeddings of multiple modalities of data to a common latent space henceextracting semantic relationships between them. The embedding specific to amodality is first extracted and subsequently a constrained optimizationprocedure is performed to project the two embedding spaces to a commonmanifold. The individual embeddings are generated back from this common latentspace. However, in order to enable independent conditional inference forseparately extracting the corresponding embeddings from the common latent spacerepresentation, we deploy a proxy variable trick - wherein, the single sharedlatent space is replaced by the respective separate latent spaces of eachmodality. We design an objective function, such that, during training we canforce these separate spaces to lie close to each other, by minimizing thedistance between their probability distribution functions. Experimental resultsdemonstrate that the learned joint model can generalize to learning concepts ofdouble MNIST digits with additional attributes of colors,from both textual andspeech input.

Conditional generation of multi-modal data using constrained embedding space mapping