Hypernetwork-Guided Data Augmentation for Hyperparameter Selection
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Solution Overview
Problem
Existing deep learning models face inefficiencies due to manual selection of data augmentation methods and hyperparameter combinations, leading to unnecessary computation time and resource wastage, as well as degraded performance.
Innovation Solution
A computing method and device that utilize a hypernetwork to automatically select optimal hyperparameter combinations, converting input data into augmentation data and generating primary network parameters, with the hypernetwork parameters being trained to optimize model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual selection of data augmentation methods and hyperparameter combinations is used, then model performance can be optimized, but computation time and computational resources are significantly increased
Solution Approach 1:
The system automatically selects data augmentation methods and hyperparameter combinations without requiring manual intervention. The hypernetwork dynamically determines the optimal augmentation strategy based on the input data characteristics, enabling the system to self-optimize the training process while maintaining high model performance and reducing computation time.
Solution Approach 2:
The patent changes the approach from fixed manual hyperparameter selection to dynamic hyperparameter optimization. The hypernetwork learns to map input data to optimal hyperparameter combinations, allowing the system to adaptively adjust data augmentation parameters based on the specific characteristics of the input data, thereby improving efficiency without sacrificing performance.
2Reliability
If multiple deep learning models are trained for different hyperparameter combinations, then optimal performance can be achieved, but computational resources and manpower are significantly consumed
Solution Approach 1:
The hypernetwork serves as a universal model that can generate different data augmentation strategies and hyperparameter combinations for various training scenarios. Instead of training separate deep learning models for each hyperparameter combination, the hypernetwork provides a single multi-functional system that adapts to different data characteristics and task requirements, significantly reducing computational resource consumption.
Solution Approach 2:
The patent segments the hyperparameter optimization function from the main deep learning model training. By separating the hyperparameter selection task into a dedicated hypernetwork, the system avoids the need to train multiple complete deep learning models for different hyperparameter combinations. The hypernetwork acts as a meta-model that generates optimized hyperparameters for each specific training instance.
3Quantity of substance
If inappropriate data augmentation methods or hyperparameter combinations are selected, then training data can be generated, but model performance degrades and computational resources are wasted
Solution Approach 1:
The hypernetwork incorporates feedback mechanisms that evaluate the effectiveness of generated training data and adjust hyperparameter combinations accordingly. By continuously monitoring model performance and training data quality, the system can identify and correct inappropriate data augmentation strategies, ensuring that only effective hyperparameter combinations are used to generate training data, thereby maintaining high model performance while maximizing the utility of computational resources.
Data Source
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AI summary
A computing method for a computing device includes converting input data into augmentation data according to a hyperparameter combination, and inputting the augmentation data into a primary network. A hypernetwork is configured to use a plurality of hypernetwork parameters to output a plurality of primary network parameters of the primary network according to the hyperparameter combination. The primary network is configured to use the primary network parameters to generate output data according to the augmentation data. The hypernetwork parameters are trained or being trained; the primary network parameters are untrained.