Meta-testing of representations learned using self-supervised tasks

One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of:

• performing a first set of training iterations to convert a prediction learning network into a first trained prediction learning network based on a first support set of training data, wherein the first support set of training data is associated with a first set of classes;
• executing a first trained representation learning network to convert a first data sample into a first latent representation, wherein the first trained representation learning network is generated by training a representation learning network using a first query set of training data, a first set of self-supervised losses associated with the first query set of training data, and a first set of supervised losses associated with the first query set of training data, and wherein the first query set of training data is associated with a second set of classes that is different from the first set of classes;
• and
• executing the first trained prediction learning network to convert the first latent representation into a first prediction of a class that is not included in the second set of classes.

A system, comprising:

• one or more memories that store instructions, and
• one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to perform the steps of:
• performing a first set of training iterations to convert a prediction learning network into a first trained prediction learning network based on a first support set of training data, wherein the first support set of training data is associated with a first set of classes;
• executing a first trained representation learning network to convert a first data sample into a first latent representation, wherein the first trained representation learning network is generated by training a representation learning network using a first query set of training data, a first set of self-supervised losses associated with the first query set of training data, and a first set of supervised losses associated with the first query set of training data, and wherein the first query set of training data is associated with a second set of classes that is different from the first set of classes;
• and
• executing the first trained prediction learning network to convert the first latent representation into a first prediction of a class that is not included in the second set of classes.