Category learning neural networks

A system, comprising:

• a data processing apparatus;
• a memory in data communication with the data processing apparatus and storing instructions that cause the data processing apparatus to perform operations comprising:
• training a categorization neural network to determine trained values of the categorization neural network parameters from initial values of the categorization neural network parameters, comprising, at each of a plurality of iterations:
• processing an image depicting an object using the categorization neural network in accordance with current values of categorization neural network parameters to generate an output comprising:
• (i) a current prediction for whether the image depicts an object or a background region, and (ii) a current embedding of the image;
• determining a plurality of current cluster centers based on the current values of the categorization neural network parameters, wherein each cluster center represents a respective semantic category;
• determining a gradient of an objective function that includes a classification loss and a clustering loss, wherein the classification loss depends on the current prediction for whether the image depicts an object or a background region, and wherein the clustering loss depends on a similarity between the current embedding of the image and the current cluster centers;
• and
• determining an update to the current values of the categorization neural network parameters from the gradient;
• determining a plurality of final cluster centers based on the trained values of the categorization neural network parameters;
• for each of a plurality of images, processing the image using the categorization neural network in accordance with the trained values of the categorization neural network parameters to generate a final embedding of the image;
• and
• determining a clustering of the images into the plurality of semantic categories using the final embeddings of the images and the final cluster centers.

One or more computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:

• training a categorization neural network to determine trained values of the categorization neural network parameters from initial values of the categorization neural network parameters, comprising, at each of a plurality of iterations:
• processing an image depicting an object using the categorization neural network in accordance with current values of categorization neural network parameters to generate an output comprising:
• (i) a current prediction for whether the image depicts an object or a background region, and (ii) a current embedding of the image;
• determining a plurality of current cluster centers based on the current values of the categorization neural network parameters, wherein each cluster center represents a respective semantic category;
• determining a gradient of an objective function that includes a classification loss and a clustering loss, wherein the classification loss depends on the current prediction for whether the image depicts an object or a background region, and wherein the clustering loss depends on a similarity between the current embedding of the image and the current cluster centers;
• and
• determining an update to the current values of the categorization neural network parameters from the gradient;
• determining a plurality of final cluster centers based on the trained values of the categorization neural network parameters;
• for each of a plurality of images, processing the image using the categorization neural network in accordance with the trained values of the categorization neural network parameters to generate a final embedding of the image;
• and
• determining a clustering of the images into the plurality of semantic categories using the final embeddings of the images and the final cluster centers.