Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a IT machine learning model. One of the methods includes receiving training data comprising a plurality of unlabeled training inputs and a plurality of labeled training inputs; generating augmented training data, comprising generating, for each of the plurality of unlabeled training inputs, a respective augmented training input by applying a data augmentation technique to the unlabeled training input; and training the machine learning model on the augmented training data. In particular, but not exclusively, the model may be trained for perceptual tasks (e.g. tasks relating to vision or speech).

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using meta pseudo-labels. One of the methods includes training a student neural network using pseudo-labels generated by a teacher neural network that is being trained jointly with the student neural network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, used to implement a self-training technique for generating neural network (NN) models. A first model is generated in response to training a first NN using labeled data. A respective pseudo label is generated for each item of unlabeled data when items of unlabeled data are processed using the first model. A second NN is used to process each item of a combined dataset to train the second NN. The combined dataset includes items of labeled data and a corresponding item for each respective pseudo label. Attributes of items in the combined dataset are modified to inject noise into the combined dataset when the second NN is trained. A second model is generated after the second NN is trained by processing items in the combined dataset, including processing items that represent the noise injected into the combined dataset.