Patent ID: 11966839
Assignee: DEEPMIND TECHNOLOGIES LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 12:
13. A method of training a neural network system to encode a probability density estimate for a output image, the method comprising:
training a convolutional neural network to iteratively generate a succession of values of pixels of a output image conditioned upon previously generated values of the output image, wherein the training encodes a probability density estimate for the output image in weights of the convolutional neural network; wherein the training further comprises:
encoding support data from input data, the input data defining one or more examples of a target image for the neural network system, wherein each example is an example image that is different from the output image, to generate encoded support data that comprises one or more support patches for each of the examples by processing each of the one or more examples using a neural network to generate features of the one or more examples;
encoding a combination of local context data derived from the previously generated values of the output image, and the encoded support data comprising the features of the one or more examples, to determine an attention-controlled context function that assigns a respective weight to each of the one or more support patches for each of the examples, and
conditioning one or more layers of the convolutional neural network upon the attention-controlled context function.