Abstract: An apparatus, method and computer program is described comprising: initialising weights of a target encoder based on a source encoder; initialising weights of a target discriminator associated with the target encoder such that the target discriminator is initialised to match a source discriminator associated with the source encoder; applying some of a target data set to the target encoder to generate target encoder outputs; applying the target encoder outputs to the target discriminator to generate a first local loss function output; training the target encoder to seek to increase the first local loss function output; training the target discriminator to seek to decrease the first local loss function output; and synchronising weights of the target discriminator and the source discriminator.

Abstract: An apparatus that operates in a first mode of operation to enable performance of a first predetermined task to transfer data via a transmitter device to a receiver device across a first communication channel using a first artificial neural network, wherein the first artificial neural network is partitioned to the transmitter device and the receiver device, and operate in a second mode of operation to enable performance of a second predetermined task to transfer data via the transmitter device to the receiver device across a second communication channel using a second artificial neural network, wherein the second artificial neural network is partitioned to the transmitter device and the receiver device, and determine to operate the apparatus in the first mode or the second mode.

Abstract: An apparatus comprising means for: using a generative neural network, trained to translate first sensor data to simulated second sensor data, to translate input first sensor data from a first sensor to simulated second sensor data; and providing the simulated second sensor data to a different, specific-task, machine-learning model for processing at least second sensor data.

Abstract: An apparatus, method and computer program is described comprising: initialising weights of a target encoder based on a source encoder; initialising weights of a target discriminator associated with the target encoder such that the target discriminator is initialised to match a source discriminator associated with the source encoder; applying some of a target data set to the target encoder to generate target encoder outputs; applying the target encoder outputs to the target discriminator to generate a first local loss function output; training the target encoder to seek to increase the first local loss function output; training the target discriminator to seek to decrease the first local loss function output; and synchronising weights of the target discriminator and the source discriminator.

Abstract: An apparatus comprising means for: using a generative neural network, trained to translate first sensor data to simulated second sensor data, to translate input first sensor data from a first sensor to simulated second sensor data; and providing the simulated second sensor data to a different, specific-task, machine-learning model for processing at least second sensor data.

Abstract: An apparatus that operates in a first mode of operation to enable performance of a first predetermined task to transfer data via a transmitter device to a receiver device across a first communication channel using a first artificial neural network, wherein the first artificial neural network is partitioned to the transmitter device and the receiver device, and operate in a second mode of operation to enable performance of a second predetermined task to transfer data via the transmitter device to the receiver device across a second communication channel using a second artificial neural network, wherein the second artificial neural network is partitioned to the transmitter device and the receiver device, and determine to operate the apparatus in the first mode or the second mode.