Hypernetwork Data Augmentation for Limited Training Data
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Solution Overview
Problem
The lack of sufficient training data poses a significant challenge in accurately detecting objects in images using machine learning, particularly in computer vision applications, as neural networks require a large amount of labeled data to learn correlations effectively.
Innovation Solution
A machine learning method and system that utilizes a hypernetwork to iteratively update hyperparameters and classification model parameters through data augmentation, allowing for the generation of additional training samples and automatic adjustment of augmentation parameters using gradient descent, thereby enhancing the training process without relying on extensive data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large amount of training data is used to improve detection accuracy, then the accuracy requirement is met, but the difficulty of obtaining sufficient training data increases
Solution Approach 1:
The patent uses data augmentation to create synthetic training data by copying and transforming existing data samples. The hypernetwork generates augmented versions of training images through parameter transformation, effectively copying the structure of existing data while creating variations that expand the training dataset without requiring additional real-world data collection.
Solution Approach 2:
The patent transforms training data by changing parameters such as image augmentation factors (rotation angles, scaling factors, etc.). The hyperparameter transformation module adjusts these parameters to generate diverse training samples from a limited original dataset, thereby improving detection accuracy without increasing data collection complexity.
2Quantity of substance
If data augmentation is used to increase training data quantity, then the training data sufficiency is improved, but the complexity of the training process increases
Solution Approach 1:
The patent introduces a hypernetwork as an intermediary component that bridges the gap between limited training data and the need for abundant data. The hypernetwork takes existing training data and transforms it into augmented data through learned parameter transformations, serving as a mediator that automatically handles the data augmentation process without requiring complex manual intervention.
Solution Approach 2:
The patent implements a feedback mechanism where the classification model's performance is used to guide the hyperparameter transformation. The hyperparameter is updated based on the classification results, creating a feedback loop that continuously optimizes the data augmentation process. This feedback mechanism simplifies the training process by automatically adjusting augmentation parameters based on actual model performance.
3Measurement precision
If manual adjustment of augmentation parameters is used, then the control precision is improved, but the time consumption increases
Solution Approach 1:
The patent implements self-service by allowing the system to automatically adjust augmentation parameters without manual intervention. The hyperparameter transformation module learns optimal transformation parameters from the training data and model performance, enabling the system to self-optimize the data augmentation process. This eliminates the need for manual parameter tuning while maintaining high precision in the augmentation process.
Solution Approach 2:
The patent replaces manual mechanical adjustment of parameters with an automated computational system. Instead of manually tuning augmentation parameters, the system uses a hypernetwork and optimization algorithms to automatically determine the best parameters. This substitution of manual mechanical adjustment with automated computational processes significantly reduces time consumption while maintaining or improving parameter adjustment precision.
Data Source
AI summary
A machine learning method includes steps of: (a) obtaining initial values of hyperparameters and hypernetwork parameters; (b) generating first classification model parameters according to the hyperparameters and the hypernetwork parameters, and updating the hypernetwork parameters according to a classification result based on the first classification model parameters relative to a training sample; (c) generating second classification model parameters according to the hyperparameters and the updated hypernetwork parameters, and updating the hyperparameters according to another classification result based on the second classification model parameters relative to a verification sample; and (d) repeating the steps (b) and (c) for updating the hypernetwork parameters and the hyperparameters.