Patent ID: 11875269
Assignee: DEEPMIND TECHNOLOGIES LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 24:
25. A method of generating a data item using a generator neural network, the method comprising:
receiving a set of latent values;
processing the set of latent values using the generator neural network to generate a data item;
wherein the generator neural network is jointly trained with an encoder neural network configured to generate a set of latent values for a respective data item and a discriminator neural network configured to distinguish between samples generated by the generator network and samples of the distribution which are not generated by the generator network;
wherein the generator neural network is configured to generate, based on a set of latent values, data items which are samples of a distribution representing a set of training data items; and
wherein the joint training comprises:
generating, using the encoder neural network, a set of latent values;
generating, from the set of latent values generated by the encoder neural network, an output data item using the generator neural network;
processing an input pair comprising the set of latent values generated by the encoder neural network and the output data item generated by the generator neural network using the discriminator neural network to generate (i) based on the set of latent values and the output data item, a first score that indicates whether the output data item in the input pair is a sample generated by the generator network or a sample of the distribution which is not generated by the generator network and (ii) based on the set of latent values and not on the output data item, a second score that indicates whether the set of latent values in the input are generated by the encoder neural network or sampled from a latent probability distribution; and
training the generator neural network and the encoder neural network on a loss function comprising a joint discriminator loss term based upon the first score that indicates whether the output data item in the input pair is a sample generated by the generator network or a sample of the distribution which is not generated by the generator network and a first single discriminator loss term based upon the second score that indicates whether the set of latent values in the input are generated by the encoder neural network or are sampled from a latent probability distribution.