Method and apparatus for quantifying sample difficulty based on pre-trained models

A computer implemented method for quantifying sample difficulty based on at least one pre-trained model, comprising the following steps:

• obtaining a training set for a downstream task, the training set including a plurality of training samples;
• modeling training data distributions in a feature space of the at least one pre-trained model with and without conditioning on class-related information;
• and
• quantifying a learning difficulty of each sample in the training set based at least on the training data distributions.

A computer system, comprising:

• one or more processors;
• and
• one or more storage devices storing computer-executable instructions that, when executed, cause the one or more processors to perform a method for quantifying sample difficulty based on at least one pre-trained model, the method including the following steps:
• obtaining a training set for a downstream task, the training set including a plurality of training samples,
• modeling training data distributions in a feature space of the at least one pre-trained model with and without conditioning on class-related information, and
• quantifying a learning difficulty of each sample in the training set based at least on the training data distributions.

One or more non-transitory computer readable storage media on which are stored computer-executable instructions for quantifying sample difficulty based on at least one pre-trained model, the instructions, when executed by one or more processors, causing the one or more processors to perform the following steps:

• obtaining a training set for a downstream task, the training set including a plurality of training samples;
• modeling training data distributions in a feature space of the at least one pre-trained model with and without conditioning on class-related information;
• and
• quantifying a learning difficulty of each sample in the training set based at least on the training data distributions.