Generic learning architecture for robust temporal and domain-based transfer learning

A system comprising:

• a non-transitory memory;
• and
• one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
• training a first computer model based on a first set of training data having a first characteristic;
• determining a knowledge transfer type for transferring knowledge from the first computer model to a second computer model;
• obtaining a second set of training data based on the determined knowledge transfer type, wherein the second set of training data has a second characteristic different than the first characteristic;
• and
• re-training the first computer model to produce the second computer model using the second set of training data.

A non-transitory machine readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:

• generating a first computer model that produces an outcome based on a set of input data related to a first set of features;
• training the first computer model based on a first set of training data having a first characteristic;
• determining a knowledge transfer type for transferring knowledge from the first computer model to a second computer model;
• obtaining a second set of training data based on the determined knowledge transfer type, wherein the second set of training data has a second characteristic different than the first characteristic;
• and
• enhancing the first computer model to produce the second computer model by re-training the first computer model using the second set of training data.