Method and apparatus for semi-supervised learning

A computer-implemented method for training a machine learning (ML) model using labelled and unlabelled data, the method comprising:

• obtaining a set of training data comprising a set of labelled data items and a set of unlabelled data items;
• training a loss module of the ML model using labels in the set of labelled data items, to generate a trained loss module capable of estimating a likelihood of a label for a data item, wherein the task module outputs a predicted label for a data item in the set of unlabelled data items, and the loss module is trained further using the predicted label for the data item in the set of unlabelled data items;
• training a task module of the ML model using the trained loss module, the set of labelled data items, and the set of unlabelled data items, to generate a trained task module capable of making a prediction of a label for input data, wherein the task module outputs both the predicted label for the data item in the set of unlabelled data items and a predicted label for a data item in the set of labelled data items;
• and
• wherein the training of the loss module and the training of the task module comprises alternating between a step to train the loss module and a step to train the task module for a fixed number of iterations or until convergence.

An apparatus for training a machine learning (ML) model using labelled and unlabelled data, the apparatus comprising:

• at least one memory;
• and
• at least one processor coupled to the at least one memory and configured to:
• obtain a set of training data comprising a set of labelled data items and a set of unlabelled data items,
• train a loss module of the ML model using labels in the set of labelled data items, to generate a trained loss module capable of estimating a likelihood of a label for a data item, and
• train a task module of the ML model using the loss module, the set of labelled data items, and the set of unlabelled data items, to generate a trained task module capable of making a prediction of a label for input data,
• wherein the at least one processor is configured to train the task module by:
• inputting a data item in the set of labelled data items into the task module and outputting, from the task module, a predicted label for the data item in the set of labelled data items;
• comparing, using a supervised loss module, the output predicted label for the data item in the set of labelled data items with an actual label of the data item in the set of labelled data items, and determining a supervised loss function based on a result of the comparing;
• inputting a data item in the set of unlabelled data items into the task module and outputting, from the task module, a predicted label for the data item in the set of unlabelled data items;
• determining an unsupervised loss function using the trained loss module and the output predicted label, output from the task module, for the data item in the set of unlabelled data items, wherein the unsupervised loss function is defined by a likelihood of the predicted label for the data item in the set of unlabelled data items;
• calculating a total loss function based on a sum of the supervised loss function and the unsupervised loss function;
• and
• using the total loss function and backpropagation to train the task module by minimizing the sum of the supervised loss function and the unsupervised loss function.