Model Performance Evaluation Using Unlabeled Data and Adversarial Noise
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Solution Overview
Problem
Current performance evaluation methods for models rely on labeled datasets, which do not accurately reflect real-world environments and require frequent updates due to changing data distributions, leading to high time and human costs.
Innovation Solution
A method and system for evaluating model performance using unlabeled datasets through unsupervised domain adaptation, generating pseudo labels with adaptive adversarial noise, and selecting suitable models for target domains without access to labeled training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a labeled dataset is used for performance evaluation, then the evaluation can be performed with existing methods, but the evaluation dataset does not accurately reflect real-world data distribution and requires continuous updates
Solution Approach 1:
The patent creates a synthetic evaluation dataset that copies the statistical properties and distribution characteristics of real-world target domain data without actually collecting real labeled data. The synthetic data is generated through a generator network that learns from source domain data and transforms it to match target domain distribution, providing an accurate reflection of real-world conditions without the need for actual field data collection.
Solution Approach 2:
The patent changes the fundamental parameters of data generation by using a learnable transformation approach. Instead of using static pre-labeled datasets, the system dynamically generates evaluation data by transforming source domain data through domain adaptation parameters, allowing the evaluation dataset to adapt to changing real-world data distributions over time.
2Measurement precision
If a labeled dataset in the real environment is prepared as the evaluation dataset, then the evaluation reflects real-world conditions, but considerable time and human costs are required for labeling
Solution Approach 1:
The system enables self-service evaluation by automatically generating synthetic labeled data through the learned transformation model. The generator network automatically creates evaluation datasets that reflect real-world conditions without requiring human annotators to manually label data, significantly reducing time and human resource costs while maintaining evaluation accuracy.
Solution Approach 2:
The patent performs preliminary domain adaptation training to learn the transformation parameters before actual evaluation. By pre-learning the mapping from source domain to target domain distribution, the system can quickly generate synthetic evaluation data without requiring time-consuming manual labeling processes during the evaluation phase.
3Measurement precision
If the evaluation dataset is continuously updated to reflect changing data distributions, then the model performance evaluation remains accurate, but time and resources must be continuously invested
Solution Approach 1:
The patent uses learnable transformation parameters that can be efficiently updated to adapt to changing data distributions. Instead of completely regenerating the evaluation dataset from scratch, the system updates the domain adaptation parameters to reflect new distribution characteristics, allowing for efficient continuous adaptation with minimal computational resources and time investment.
Data Source
AI summary
A method for a performance evaluation may include: obtaining a first model trained using a labeled dataset of a source domain; obtaining a second model built by performing domain adaptation to a target domain on the first model; generating a pseudo label for an evaluation dataset of the target domain using the second model; and evaluating performance of the first model using the pseudo label. The evaluation dataset is an unlabeled dataset, and the generating of the pseudo label may include adjusting an upper limit of a size constraint of adversarial noise; deriving the adversarial noise for a data sample belonging to the evaluation dataset within a range that satisfies the size constraint; 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.