Training generative adversarial networks
AssigneeInternational Business Machines Corporation
InventorOmid Bonakdar Sakhi
Life of this patent
Estimating the term…
Abstract
Improving the training of generative adversarial networks by identifying the input and output layers in the generator network, assigning a gradient multiplier to each layer, and multiplying the gradient by the multiplier during back-propagation and before applying the product of the gradient and multiplier to node weights of the layers.
Claims17
A computer implemented method for training machine learning systems, the method comprising:
- identifying, by one or more computer processors, an input layer in a generator network;
- assigning, by the one or more computer processors, a unique multiplier to a first network layer after the input layer, wherein the unique multiplier comprises a value associated with an inverse of a number of training set data instances;
- applying, by the one or more computer processors, a gradient and the unique multiplier to a node weight of the first network layer during generator network back-propagation;
- training, by the one or more computer processors, the generator network having the node weight;
- providing, by the one or more computer processors, the trained generator network an input;
- generating, by the trained generator network, an output;
- and
- displaying the output to a user.
A computer program product for training machine learning systems, the computer program product comprising one or more computer readable storage devices and program instructions collectively stored on the one or more computer readable storage devices, the stored program instructions comprising:
- program instructions to identify an input layer in a generator network;
- program instructions to assign a unique multiplier to a network layer after the input layer, wherein the unique multiplier comprises a value associated with an inverse of a number of training set data instances;
- program instructions to apply a gradient and the unique multiplier to a node weight of the network layer during generator network back-propagation;
- program instructions to train the generator network having the node weight;
- program instructions to provide the trained generator network an input;
- program instructions to generate an output;
- and
- program instructions to display the output to a user.
A computer system for training machine learning systems, the computer system comprising:
- one or more computer processors;
- one or more computer readable storage devices;
- and
- stored program instructions on the one or more computer readable storage devices for execution by the one or more computer processors, the stored program instructions comprising:
- program instructions to identify an input layer in a generator network;
- program instructions to assign a unique multiplier to a network layer after the input layer, wherein the unique multiplier comprises a value associated with an inverse of a number of training set data instances;
- and
- program instructions to apply a gradient and the unique multiplier to a node weight of the network layer during back-propagation;
- program instructions to train the generator network having the node weight;
- program instructions to provide the trained generator network an input;
- program instructions to generate an output;
- and
- program instructions to display the output to a user.
Dependencies are inferred from the claim text.