Artificial intelligent systems and methods for using a structurally simpler learner model to mimic behaviors of a structurally more complicated reference model

An artificial intelligent method for using a structurally simpler learner model to mimic behaviors of a structurally more complicated reference model, comprising:

• obtaining a trained reference model at least including N reference blocks, wherein N is an integer greater than 2;
• obtaining a learner model at least including N learner blocks respectively corresponding to the N reference blocks of the trained reference model;
• training the learner model into a trained learner model by conducting an iterative operation by:
• inputting a sample data set into the trained reference model and the learner model;
• for each of the N learner blocks:
• determining a reference vector based on an output of the reference block corresponding to the learner block,
• determining a learner vector based on an output of the learner block, wherein a length of the reference vector equals a length of the learner vector,
• determining a distance between the reference vector and the learner vector to obtain a learning signal, and
• updating parameters in the learner block by inputting the distance into the learner block as a learning signal;
• determining whether the learner model is convergent;
• and
• in response to a determination that the learner model is convergent, stopping the iterative operation to assign the learner model as the trained learner model.

A non-transitory computer readable medium, comprising at least one set of artificially intelligent instructions compatible for using a structurally simpler learner model to mimic behaviors of a structurally more complicated reference model, wherein when executed by at least one processor of one or more electronic device, the at least one set of instructions directs the at least one processor to:

• obtain a trained reference model at least including N reference blocks, wherein N is an integer greater than 2;
• obtain a learner model at least including N learner blocks respectively corresponding to the N reference blocks of the trained reference model;
• train the learner model into a trained learner model by conducting an iterative operation by:
• inputting a sample data set into the trained reference model and the learner model;
• for each of the N learner blocks:
• determining a reference vector based on an output of the reference block corresponding to the learner block, determining a learner vector based on an output of the learner block, wherein a length of the reference vector equals a length of the learner vector, determining a distance between the reference vector and the learner vector to obtain a learning signal, and updating parameters in the learner block by inputting the distance into the learner block as a learning signal;
• determine whether the learner model is convergent;
• and in response to a determination that the learner model is convergent, stop the iterative operation to assign the learner model as the trained learner model.