Method and apparatus for stochastic inference between multiple random variables via common representation

Abstract

A method and system are herein disclosed. The method includes developing a joint latent variable model having a first variable, a second variable, and a joint latent variable representing common information between the first and second variables; generating a variational posterior of the joint latent variable model; training the variational posterior; performing inference of the first variable from the second variable based on the variational posterior, wherein performing the inference comprises conditionally generating the first variable from the second variable; and extracting common information between the first variable and the second variable, wherein extracting the common information comprises adding a regularization term to a loss function.

What is claimed is: 
     
         1 . A method, comprising:
 developing a joint latent variable model having a first variable, a second variable, and a joint latent variable representing common information between the first and second variables;   generating a variational posterior of the joint latent variable model:   training the variational posterior;   performing inference of the first variable from the second variable based on the variational posterior, wherein performing the inference comprises conditionally generating the first variable from the second variable; and   extracting common information between the first variable and the second variable, wherein extracting the common information comprises adding a regularization term to a loss function.   
     
     
         2 . The method of  claim 1 , further comprising adding local randomness to the joint latent variable model. 
     
     
         3 . The method of  claim 2 , wherein adding the local randomness comprises separating the joint latent variable into a common latent variable and a local latent variable. 
     
     
         4 . The method of  claim 1 , wherein performing the inference comprises generating a style for the first variable or the second variable. 
     
     
         5 . The method of  claim 1 , wherein training the variational posterior comprises training a decoder in the joint latent variable model with a full approximate posterior of the joint latent variable model. 
     
     
         6 . The method of  claim 5 , wherein training the variational posterior further comprises fixing parameters of the decoder and training a marginal variational posterior with the trained decoder. 
     
     
         7 . The method of  claim 1 , wherein training the variational posterior comprises training the joint latent variable model, a full approximate posterior, and a marginal variational posterior jointly using a hyperparameter. 
     
     
         8 . A system, comprising:
 a decoder:   an encoder; and   a processor configured to:
 develop a joint latent variable model having a first variable, a second variable, and a joint latent variable representing common information between the first and second variables, 
 generate a variational posterior of the joint latent variable model, 
 train the variational posterior, 
 perform inference of the first variable from the second variable based on the variational posterior, by conditionally generating the first variable from the second variable, and 
 extract common information between the first variable and the second variable, wherein extracting the common information comprises adding a regularization term to a loss function. 
   
     
     
         9 . The system of  claim 8 , wherein the processor is further configured to add local randomness to the joint latent variable model. 
     
     
         10 . The system of  claim 9 , wherein the processor is further configured to add the local randomness by separating the joint latent variable into a common latent variable and a local latent variable. 
     
     
         11 . The system of  claim 8 , wherein the processor is further configured to perform the inference by generating a style for the first variable or the second variable. 
     
     
         12 . The system of  claim 8 , wherein the processor is further configured to train the variational posterior by training the decoder in the joint latent variable model with a full approximate posterior of the joint latent variable model. 
     
     
         13 . The system of  claim 12 , wherein the processor is further configured to train the variational posterior by fixing parameters of the decoder and training a marginal variational posterior with the trained decoder. 
     
     
         14 . The system of  claim 9 , wherein the processor is further configured to train the variational posterior by training the joint latent variable model, a full approximate posterior, and a marginal variational posterior jointly using a hyperparameter. 
     
     
         15 . A non-transitory computer-readable media configured to store instructions, which when executed, control a processor to:
 develop a joint latent variable model having a first variable, a second variable, and a joint latent variable representing common information between the first and second variables;   generate a variational posterior of the joint latent variable model;   train the variational posterior;   perform inference of the first variable from the second variable based on the variational posterior, wherein performing the inference comprises conditionally generating the first variable from the second variable; and   extract common information between the first variable and the second variable, wherein extracting the common information comprises adding a regularization term to a loss function.   
     
     
         16 . The non-transitory computer-readable media of  claim 15 , wherein the instructions, when executed, further control the processor to add local randomness to the joint latent variable model. 
     
     
         17 . The non-transitory computer-readable media of  claim 16 , wherein the instructions, when executed, further control the processor to add the local randomness by separating the joint latent variable into a common latent variable and a local latent variable. 
     
     
         18 . The non-transitory computer-readable media of  claim 15 , wherein the instructions, when executed, further control the processor to perform the inference by generating a style for the first variable or the second variable. 
     
     
         19 . The non-transitory computer-readable media of  claim 15 , wherein the instructions, when executed, further control the processor to train the variational posterior by training a decoder in the joint latent variable model with a full approximate posterior of the joint latent variable model. 
     
     
         20 . The non-transitory computer-readable media of  claim 19 , wherein the instructions, when executed, further control the processor to train the variational posterior by fixing parameters of the decoder and training a marginal variational posterior with the trained decoder.