Method and device with bayesian meta continual-learning and inferring

An apparatus for meta continual-learning and inferring using an implementation of Bayes' theorem, the apparatus comprising:

• a data distribution learner for calculating likelihood of learning data for a given latent variable;
• a Bayes' calculator for performing a sequential Bayesian update and determining a final posterior distribution of the latent variable by using prior distribution of the latent variable and the calculated likelihood;
• and
• an inference engine for inferring test output data based on a latent variable sampled from the final posterior distribution and test input data,
• wherein respective meta parameter of a neural network of the data distribution learner, the prior distribution of the latent variable of the Bayes' calculator, and a neural network of the inference engine are trained by a meta learning.

A method of meta continual-learning and inferring method using an implementation of Bayes' theorem, the method comprising:

• generating an episode for meta learning;
• inputting given learning data in the episode;
• calculating a likelihood of the learning data for a given latent variable by a data distribution learner;
• performing a sequential Bayesian update and determining a final posterior distribution of the latent variable based on prior distribution of the latent variable and the calculated likelihood;
• sampling the latent variable from the final posterior distribution;
• inferring, by a model, test output data based on the sampled latent variable and test input data;
• calculating a lower bound of an objective function for the test output data;
• and
• learning respective meta parameters of a neural network of the data distribution learner, prior distribution of the latent variable of the Bayes' calculator, and a neural network of the inference engine through meta learning by maximizing the lower bound of the objective function.