Clustered encoding and decoding from a latent probability distribution

A computer program product for learning a model for clustered encoding and decoding from a latent probability distribution, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• map, by the processor, high-dimensional modalities of data from one or more latent probability distributions corresponding to a plurality of encoder and decoder pairs to a plurality of independent latent spaces;
• and
• map, by the processor, the plurality of independent latent spaces to a common latent space representing one or more features of one or more input classes associated with the plurality of encoder and decoder pairs,
• wherein the latent probability distribution represents a likelihood of different multi-dimensional images input to each of encoder of the plurality of encoder and decoder pairs via multi-stage latent learning;
• and
• multiply, by the processor, the plurality of independent latent spaces by a plurality of learnable weight matrices and combine, by the processor, the resulting products to generate the common latent space.

A system for learning a model for clustered encoding and decoding from a latent probability distribution, comprising:

• a memory that stores computer executable components;
• and
• a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
• a plurality of encoder and decoder pairs that map high-dimensional modalities of data from a latent probability distribution to a plurality of independent latent spaces, wherein the processor can combine the plurality of independent latent spaces to generate a common latent space that represents one or more features of one or more input classes associated with the plurality of encoder and decoder pairs;
• and the latent probability distribution represents a likelihood of different multi-dimensional images input to each encoder of the plurality of encoder and decoder pairs via multi-stage latent learning;
• and wherein the processor initializes a plurality of learnable weight matrices with a random variety of weights equal to a shape of a first one of the plurality of independent latent spaces, and wherein the processor multiplies the plurality of independent latent spaces by the plurality of learnable weight matrices and combines the resulting products to generate the common latent space.