Method and system for learning perturbation sets in machine learning
AssigneeRobert Bosch GmbH
InventorsEric WONG, Jeremy KOLTER, Wan-Yi LIN
Life of this patent
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Abstract
A computer-implemented method for training a neural network, comprising receiving an input data, defining a perturbed version of the input data in response to a dimensional latent vector and the input data, training a variational auto encoder (VAE) utilizing the perturbed version of the input data, wherein the VAE outputs, utilizing an encoder, a latent vector in response to the input data and the perturbed version of the input data, decoding the latent vector, utilizing a decoder of the VAE, back to an input latent space to output a perturbed example, and outputting a learned perturbed set utilizing one or more perturbed examples and upon convergence to a first threshold.
Claims20
A computer-implemented method for training a neural network, comprising:
- receiving an input data;
- defining a perturbed version of the input data in response to a dimensional latent vector and the input data;
- training a variational auto encoder (VAE) utilizing the perturbed version of the input data, wherein the VAE outputs, utilizing an encoder, a latent vector in response to the input data and the perturbed version of the input data;
- decoding the latent vector, utilizing a decoder of the VAE, back to an input latent space to output a perturbed example;
- and
- outputting a learned perturbed set utilizing one or more perturbed examples and upon convergence to a first threshold.
A system including a neural network, comprising:
- an input interface configured to receive input data;
- a processor, in communication with the input interface, wherein the processor is programmed to:
- receive the input data;
- define a perturbed version of the input data in response to a dimensional latent vector and the input data;
- output a latent vector associated with the perturbed version of the input data, wherein the latent vector is output utilizing an encoder of a variational auto encoder (VAE) and in response to the input data and the perturbed version of the input data;
- decode the latent vector, utilizing a decoder of the VAE, back to an input latent space to output a perturbed example;
- output a learned perturbed set utilizing one or more perturbed examples and upon convergence to a first threshold;
- and
- train one or more classifiers of the neural network utilizing the learned perturbed set.
A computer-program product storing instructions which, when executed by a computer, cause the computer to:
- receive the input data;
- define a perturbed version of the input data in response to a dimensional latent vector and the input data;
- output a latent vector associated with the perturbed version of the input data, wherein the latent vector is output utilizing an encoder of a variational auto encoder (VAE) and in response to the input data and the perturbed version of the input data;
- decode the latent vector, utilizing a decoder of the VAE, back to an input latent space to output a perturbed example;
- and
- outputting a learned perturbed set utilizing one or more perturbed examples and upon convergence to a first threshold.
Dependencies are inferred from the claim text.