Use of a Training Framework of a Multi-Class Model to Train a Multi-Label Model
AssigneePaypal, Inc.
InventorZehong Ma
Life of this patent
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Abstract
Techniques are disclosed relating to receiving, by a computer system, an unlabeled data object to be labeled using a classification model that is trained to output a probability distribution across a plurality of classes that are treated by the classification model as mutually exclusive. The technique may further include using, by the computer system, the classification model in a manner that determines a set of non-mutually-exclusive probabilities that respective ones of the plurality of classes apply to the unlabeled data object. Additionally, the technique may include labeling, by the computer system using the set of non-mutually-exclusively probabilities, the unlabeled data object.
Claims20
A method comprising:
- receiving, by a computer system, an unlabeled data object to be labeled using a classification model that is trained to output a probability distribution across a plurality of classes that are treated by the classification model as mutually exclusive;
- using, by the computer system, the classification model in a manner that determines a set of non-mutually-exclusive probabilities that respective ones of the plurality of classes apply to the unlabeled data object;
- and
- labeling, by the computer system using the set of non-mutually-exclusively probabilities, the unlabeled data object.
A computer-readable, non-transient memory including instructions that when executed by a computer system within a computer network, cause the computer system to perform operations including:
- training a classification model to output, for a particular set of training data, a probability distribution across a plurality of classes that are treated by the classification model as being mutually exclusive;
- and
- retraining the classification model, including:
- comparing ones of the plurality of classes to the particular set of training data;
- and
- generating, using the comparing, a respective binary probability distribution that indicates a confidence that a compared one of the plurality of classes applies, independently, to the particular set of training data.
A system comprising:
- training, by a computer system, a classification model to output a probability distribution across a plurality of classes, wherein a given output of the classification model includes a first set of probabilities that a particular training data object belongs exclusively to a respective one of the plurality of classes;
- and
- retraining, by the computer system, the classification model to:
- evaluate whether ones of the plurality of classes apply to the particular training data object;
- and
- generate, using the evaluating, a second set of probabilities that respective ones of the plurality of classes apply, non-exclusively, to the particular training data object.
Dependencies are inferred from the claim text.