International Business Machines Corporation
SEMI-SUPERVISED LEARNING OF TRAINING GRADIENTS VIA TASK GENERATION

Abstract:

In an approach for augmenting a neural network with a self-supervised mechanism, a processor trains a first neural network using labeled data, the first neural network configured for a main task. A processor trains a second neural network using the labeled data and unlabeled data, the second neural network being an additional component to the first neural network. A processor computes a gradient using a second loss of the second neural network based on the unlabeled data.