Domain transfer of training data for neural networks

A non-transitory machine-readable data carrier on which is stored a computer program for training a generator network for a task of transforming data records belonging to a first domain into synthetic data records belonging to a second domain, the computer program, when executed by one or more computers and/or compute instances, cause the one or more computers and/or compute instances to perform the following steps:

• providing training data records of the first domain and training data records of the second domain;
• transforming the training data records of the first domain into synthetic data records of the second domain using the generator network;
• mapping, by a task network, both the training data records of the second domain and the synthetic data records of the second domain, to respective outputs relating to a predefined task;
• creating a respective saliency record for each of the training data records and for each of the synthetic data record, including saliencies with which portions of the training data record and of the synthetic data record respectively have contributed to the respective output of the task network;
• bringing the respective saliency records together in a pool;
• classifying, by a discriminator network, saliency records sampled from the pool according to whether they belong to a training data record or to a synthetic data record;
• evaluating, using a predefined transfer cost function, an accuracy achieved in the classification using a predefined transfer cost function;
• optimizing parameters that characterize behavior of the generator network with a goal of worsening the evaluation by the transfer cost function;
• and
• optimizing parameters that characterize behavior of the discriminator network with a goal of improving the evaluation by the transfer cost function.

One or more computers and/or compute instances configured to train a generator network for a task of transforming data records belonging to a first domain into synthetic data records belonging to a second domain, the one or more computers and/or compute instances configured to:

• provide training data records of the first domain and training data records of the second domain;
• transform the training data records of the first domain into synthetic data records of the second domain using the generator network;
• map, by a task network, both the training data records of the second domain and the synthetic data records of the second domain, to respective outputs relating to a predefined task;
• create a respective saliency record for each of the training data records and for each of the synthetic data record, including saliencies with which portions of the training data record and of the synthetic data record respectively have contributed to the respective output of the task network;
• bring the respective saliency records together in a pool;
• classify, using a discriminator network, saliency records sampled from the pool according to whether they belong to a training data record or to a synthetic data record;
• evaluate, using a predefined transfer cost function, an accuracy achieved in the classification using a predefined transfer cost function;
• optimize parameters that characterize behavior of the generator network with a goal of worsening the evaluation by the transfer cost function;
• and
• optimize parameters that characterize behavior of the discriminator network with a goal of improving the evaluation by the transfer cost function.