System and method for learning with noisy labels as semi-supervised learning
Assigneesalesforce.com, inc.
InventorsJunnan Li, Chu Hong Hoi
Life of this patent
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Abstract
A method provides learning with noisy labels. The method includes generating a first network of a machine learning model with a first set of parameter initial values, and generating a second network of the machine learning model with a second set of parameter initial values. First clean probabilities for samples in a training dataset are generated using the second network. A first labeled dataset and a first unlabeled dataset are generated from the training dataset based on the first clean probabilities. The first network is trained based on the first labeled dataset and first unlabeled dataset to update parameters of the first network.
Claims20
A method for learning with noisy labels, comprising:
- generating a first network of a machine learning model with a first set of parameter initial values;
- generating a second network of the machine learning model with a second set of parameter initial values;
- generating first clean probabilities for a first plurality of samples in a training dataset using the second network;
- generating a first labeled dataset including a second plurality of clean samples from the training dataset, wherein the second plurality of clean samples are determined based on the corresponding first clean probabilities;
- generating a first unlabeled dataset including a third plurality of noisy samples from the training dataset, wherein the third plurality of noisy samples are determined based on the corresponding first clean probabilities;
- and
- training the first network based on the first labeled dataset and the first unlabeled dataset to update parameters of the first network.
A non-transitory machine-readable medium comprising a plurality of machine-readable instructions which, when executed by one or more processors, are adapted to cause the one or more processors to perform a method comprising:
- generating a first network of a machine learning model with a first set of parameter initial values;
- generating a second network of the machine learning model with a second set of parameter initial values;
- generating first clean probabilities for a first plurality of samples in a training dataset using the second network;
- generating a first labeled dataset including a second plurality of clean samples from the training dataset, wherein the second plurality of clean samples are determined based on the corresponding first clean probabilities;
- generating a first unlabeled dataset including a third plurality of noisy samples from the training dataset, wherein the third plurality of noisy samples are determined based on the corresponding first clean probabilities;
- and
- training the first network based on the first labeled dataset and first unlabeled dataset to update parameters of the first network.
A system, comprising:
- a non-transitory memory;
- and
- one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform a method comprising:
- generating a first network of a machine learning model with a first set of parameter initial values;
- generating a second network of the machine learning model with a second set of parameter initial values;
- generating first clean probabilities for a first plurality of samples in a training dataset using the second network;
- generating a first labeled dataset including a second plurality of clean samples from the training dataset, wherein the second plurality of clean samples are determined based on the corresponding first clean probabilities;
- generating a first unlabeled dataset including a third plurality of noisy samples from the training dataset, wherein the third plurality of noisy samples are determined based on the corresponding first clean probabilities;
- and
- training the first network based on the first labeled dataset and the first unlabeled dataset to update parameters of the first network.
Dependencies are inferred from the claim text.