Fotonation Limited

Shabab Bazrafkan

Joe Lemley

Peter Corcoran

Systems and methods for training a conditional generator model are described. Methods receive a sample, and determine a discriminator loss for the received sample. The discriminator loss is based on an ability to determine whether the sample is generated by the conditional generator model or is a ground truth sample. The method determines a secondary loss for the generated sample and updates the conditional generator model based on an aggregate of the discriminator loss and the secondary loss.

Systems and methods for conditional generative models

Training a target neural network comprises providing a first batch of samples of a given class to respective instances of a generative neural network, each instance providing a variant of the sample in accordance with the parameters of the generative network. Each variant produced by the generative network is compared with another sample of the class to provide a first loss function for the generative network. A second batch of samples is provided to the target neural network, at least some of the samples comprising variants produced by the generative network. A second loss function is determined for the target neural network by comparing outputs of instances of the target neural network to one or more targets for the neural network. The parameters for the target neural network are updated using the second loss function and the parameters for the generative network are updated using the first and second loss functions.

Method of training a neural network

An image processing apparatus comprises a set of infra-red (IR) sources surrounding an image capture sensor and a processor operatively coupled to said IR sources and said image capture sensor. The processor being arranged to acquire from the sensor a succession of images, each illuminated with a different combination of the IR sources. The processor is further arranged to combine component images corresponding to the succession of images by selecting a median value for corresponding pixel locations of the component images as a pixel value for the combined image.

Image processing apparatus

Synthesizing a neural network from a plurality of component neural networks is disclosed. The method comprises mapping each component network to a respective graph node where each node is first labelled in accordance with the structure of a corresponding layer of the component network and a distance of the node from one of a given input or output. The graphs for each component network are merged into a single merged graph by merging nodes from component network graphs having the same first structural label. Each node of the merged graph is second labelled in accordance with the structure of the corresponding layer of the component network and a distance of the node from the other of a given input or output. The merged graph is contracted by merging nodes of the merged graph having the same second structural label. The contracted-merged graph is mapped to a synthesized neural network.

Growing community of inventors

Galway, Ireland

Shabab Bazrafkan