Subset conditioning using variational autoencoder with a learnable tensor train induced prior

A computer program product comprising:

• a non-transient, tangible memory device having computer-executable instructions that when executed by a processor, cause performance of a method comprising:
• providing a model configured as a variational autoencoder with tensor train,
• providing a dataset having object data for an object and condition data for a condition, wherein the condition may be a property of the object;
• processing the object data of the dataset to obtain latent object data with an object encoder of the model, wherein the latent object data includes a distribution of latent variables having a mean and a variance;
• sampling one or more latent variables from the obtained distribution of latent variables;
• processing the sampled one or more latent variables with defined object properties to compute a probability of the samples having the defined object properties;
• processing the sampled one or more latent variables through an object decoder to obtain a reconstructed object;
• determining a reconstruction loss of the reconstructed object from an original object from the object data;
• computing a Kullback-Leibler divergence from the probability of the samples having the defined object properties;
• using the determined reconstruction loss and computed Kullback-Leibler divergence to compute a loss from the data set;
• perform a gradient descent until the reconstructed object is sufficiently representative of the original object and has the defined object properties;
• obtaining a trained model configured as a trained variational autoencoder with a learnable prior that is parameterized with a tensor train;
• and
• providing the trained model.