Generative adversarial neural architecture search
AssigneeHuawei Technologies Co., Ltd.
InventorsSeyed Saeed Changiz Rezaei, Fred Xuefei Han, Di Niu
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Abstract
A method and system for neural architectural search (NAS) for performing a task. A generative adversarial network comprising a generator and a discriminator receives, from a user device, a query for neural network architecture, the query including a search space. The generator of the generative adversarial network generates a plurality of generated neural network architectures responsive to the received search space. The discriminator of the generative adversarial network selects an optimal neural network architecture from among the plurality of generated neural network architectures. The optimal generated neural network architecture is transmitted to the user device.
Claims20
A method for neural architectural search (NAS) for performing a task, the method comprising:
- receiving, by a generative adversarial network comprising a generator and a discriminator, from a user device, a query for neural network architecture, the query including a search space, wherein the discriminator of the generative adversarial network is trained by minimizing a cross-entropy loss between a truth class and predicted probabilities generated from candidate architectures from a set of true architectures or a set of fake architectures respectively;
- generating, via the generator of the generative adversarial network, a plurality of generated neural network architectures responsive to the received search space;
- selecting, via the discriminator of the generative adversarial network, an optimal neural network architecture from among the plurality of generated neural network architectures;
- and
- transmitting the optimal generated neural network architecture to the user device.
A system for neural architectural search (NAS) using a generative adversarial network, comprising:
- a processing device;
- and
- a memory coupled to the processing device, the memory storing computer-executable instructions that, in response to execution by the processing device, cause the system to:
- receive, by a generator of the generative adversarial network, from a user device, a query for neural network architecture, the query including a search space;
- and
- generate, via the generator of the generative adversarial network, a plurality of generated neural network architectures responsive to the received search space;
- and
- select, via a discriminator of the generative adversarial network, an optimal neural network architecture from among the plurality of generated neural network architectures;
- wherein the optimal generated neural network architecture is transmitted to the user device, and
- wherein the discriminator of the generative adversarial network is trained by minimizing a cross-entropy loss between a truth class and predicted probabilities generated from candidate architectures from a set of true architectures or a set of fake architectures respectively.
A non-transitory machine-readable storage medium having tangibly stored thereon executable instructions for execution by one or more processors that, in response to execution by the one or more processors, cause the one or more processors to:
- receive, by a generative adversarial network comprising a generator and a discriminator, from a user device, a query for neural network architecture, the query including a search space, wherein the discriminator of the generative adversarial network is trained by minimizing a cross-entropy loss between a truth class and predicted probabilities generated from candidate architectures from a set of true architectures or a set of fake architectures respectively;
- generate, via the generator of the generative adversarial network, a plurality of generated neural network architectures responsive to the received search space;
- select, via the discriminator of the generative adversarial network, an optimal neural network architecture from among the plurality of generated neural network architectures;
- and
- transmit the optimal generated neural network architecture to the user device.
Dependencies are inferred from the claim text.