Method for evaluating performance and system thereof
AssigneeSamsung Sds Co., Ltd.
InventorsJoon Ho Lee, Han Kyu Moon, Jae Oh Woo
Life of this patent
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Abstract
Provided are a method for evaluating performance and a system thereof. The method according to some embodiments may include obtaining a first model trained using a labeled dataset, obtaining a second model built by performing unsupervised domain adaptation on the first model, generating pseudo labels for an evaluation dataset using the second model, wherein the evaluation dataset is an unlabeled dataset, and evaluating performance of the first model using the pseudo labels.
Claims13
A method for evaluating performance, the method being performed by at least one computing device and comprising:
- obtaining a first model trained using a labeled dataset;
- obtaining a second model built by performing unsupervised domain adaptation on the first model;
- generating pseudo labels for an evaluation dataset using the second model, wherein the evaluation dataset is an unlabeled dataset;
- and
- evaluating performance of the first model using the pseudo labels,
- wherein the generating of the pseudo labels comprises:
- deriving adversarial noise for a data sample belonging to the evaluation dataset;
- generating a noisy sample by reflecting the derived adversarial noise in the data sample;
- and
- generating a pseudo label for the data sample based on a predicted label of the noisy sample obtained through the second model.
A system for evaluating performance, the system comprising:
- a memory configured to store one or more instructions;
- and
- one or more processors configured to execute the one or more stored instructions to perform:
- obtaining a first model trained using a labeled dataset;
- obtaining a second model built by performing unsupervised domain adaptation on the first model;
- generating pseudo labels for an evaluation dataset using the second model, wherein the evaluation dataset is an unlabeled dataset;
- and
- evaluating performance of the first model using the pseudo labels,
- wherein the generating of the pseudo labels comprises:
- deriving adversarial noise for a data sample belonging to the evaluation dataset;
- generating a noisy sample by reflecting the derived adversarial noise in the data sample;
- and
- generating a pseudo label for the data sample based on a predicted label of the noisy sample obtained through the second model.
A non-transitory computer-readable recording medium storing computer program executable by at least one processor to perform:
- obtaining a first model trained using a labeled dataset;
- obtaining a second model built by performing unsupervised domain adaptation on the first model;
- generating pseudo labels for an evaluation dataset using the second model, wherein the evaluation dataset is an unlabeled dataset;
- and
- evaluating performance of the first model using the pseudo labels,
- wherein the generating of the pseudo labels comprises:
- deriving adversarial noise for a data sample belonging to the evaluation dataset;
- generating a noisy sample by reflecting the derived adversarial noise in the data sample;
- and
- generating a pseudo label for the data sample based on a predicted label of the noisy sample obtained through the second model.
Dependencies are inferred from the claim text.