Learning device, learning method, recording medium for learning device, inference device, inference method, and recording medium for inference device

A learning method comprising:

• outputting a recognition result with respect to recognition object data in a dataset for learning that is a set of pairs of the recognition object data and a weak label attached to the recognition object data;
• and
• calculating a recognition loss using the recognition result, a mixing matrix calculated based on the dataset for learning, and the weak label,
• wherein the dataset for learning has a weak label probability distribution,
• wherein the weak label probability distribution is a probability distribution according to the weak label conditioned by a true correct answer class to which the recognition object data belongs and is reconfigurable,
• wherein calculating the recognition loss includes
• converting the recognition result into a conjugate vector,
• calculating a product of the conjugate vector and the mixing matrix,
• calculating a normalization term from the conjugate vector, and
• calculating a sum of the product and the normalization term and outputting the calculated sum as the recognition loss, and
• wherein outputting the recognition result further includes performing learning using the recognition loss.

A non-transitory computer-readable recording medium for a learning device in which a program for causing a computer to execute a learning method is recorded, the learning method including:

• outputting a recognition result with respect to recognition object data in a dataset for learning that is a set of pairs of the recognition object data and a weak label attached to the recognition object data;
• and
• calculating a recognition loss using the recognition result, a mixing matrix calculated based on the dataset for learning, and the weak label,
• wherein the dataset for learning has a weak label probability distribution,
• wherein the weak label probability distribution is a probability distribution according to the weak label conditioned by a true correct answer class to which the recognition object data belongs, and is reconfigurable,
• wherein calculating the recognition loss includes
• converting the recognition result into a conjugate vector,
• calculating a product of the conjugate vector and the mixing matrix,
• calculating a normalization term from the conjugate vector, and
• calculating a sum of the product and the normalization term and outputting the calculated sum as the recognition loss, and
• wherein outputting the recognition result further includes performing learning using the recognition loss.