Semi-supervised learning using clustering as an additional constraint

Abstract

In some implementations a neural network is trained to perform a main task using a clustering constraint, for example, using both a main task training loss and a clustering training loss. Training inputs are inputted into a main task neural network to produce output labels predicting locations of the parts of the objects in the training inputs. Data from pooled layers of the main task neural network is inputted into a clustering neural network. The main task neural network and the clustering neural network are trained based on a main task loss from the main task neural network and a clustering loss from the clustering neural network. The main task loss is determined by comparing differences between the output labels and the training labels. The clustering loss encourages the clustering network to learn to label the parts of the objects individually, e.g., to learn groups corresponding to the object parts.