Apparatuses, systems, and techniques to use one or more neural networks to generate data labels. In at least one embodiment, one or more neural networks is trained based, at least in part on, one or more labels 104, pseudo-labels 116, training data 102, and modified training data 114. A second neural network may obtain a second label for a datum that has a first label and such labels may indicate objects within the images. The neural networks may use both task loss and consistency loss and may further comprise weights based on weights within previous versions of the neural network. Consistency regularisation terms may be to train the neural networks and may be increased over iterations of the neural network. The training data may be a natural processing algorithm (NLP).
Neural network training using robust temporal ensembling