Deep Learning Parameter Reuse via Validation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing parameter reuse methods in deep learning, such as transfer learning and semi-supervised learning, face the challenge of blindly selecting reusable parameters, leading to poor model performance when data is limited.
Innovation Solution
A method that involves training a target model using a pre-configured dataset with a validation set, identifying correspondences between layers of a target model and a pre-trained original model with identical network structures, and validating the replaced target model to determine reusable parameters, allowing for the systematic selection and reuse of parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If transfer learning or semi-supervised learning is used to reuse parameters, then model training can be performed with limited data, but the selection of reusable parameters is blind and results in poor model performance
Solution Approach 1:
The patent segments the parameter reuse process into distinct phases: initial training with limited data, validation-based parameter selection, and selective parameter reuse. By dividing the parameter selection into individual parameter evaluations rather than bulk reuse, the method enables precise identification of which parameters can be reliably reused, thus resolving the contradiction between using limited data and maintaining model performance.
Solution Approach 2:
The patent introduces a feedback mechanism where each parameter's reusability is validated by comparing model performance metrics before and after parameter replacement. This feedback loop allows the system to learn which parameters are suitable for reuse based on their actual impact on model performance, transforming the blind selection process into an evidence-based decision-making process that maintains reliability even with limited training data.
2Productivity
If all parameters from pre-trained models are reused without validation, then training efficiency improves, but model performance deteriorates due to blind selection
Solution Approach 1:
The patent applies partial action by selectively reusing only those parameters that pass validation criteria rather than reusing all parameters from pre-trained models. This selective approach maintains training efficiency by reusing parameters when beneficial while avoiding performance degradation from inappropriate parameter reuse, thus resolving the contradiction between training efficiency and model performance.
Solution Approach 2:
The patent changes the state of parameter reuse from a binary all-or-nothing approach to a conditional approach based on validation results. By introducing performance thresholds and validation metrics as parameters that control parameter reuse, the system can dynamically adjust which parameters are reused, maintaining both training efficiency and model performance reliability.
3Reliability
If parameter validation and selection process is implemented systematically, then model performance improves, but training time and computational resources increase
Solution Approach 1:
The patent performs preliminary validation of parameters during the initial training phase before full model deployment. By pre-identifying and validating which parameters can be reused during the training process itself, the system avoids the need for extensive post-training validation, thus maintaining high model performance while minimizing additional time consumption.
Data Source
AI summary
A method, device, terminal, and storage medium for reusing parameters of a deep learning model are disclosed, the method including: training a target model with a preset training set; obtaining a pre-trained original model; obtaining correspondences between layers of the target model and the original model having identical network structures, and parameter correspondences between corresponding layers; extracting multiple original model parameters from layers of the original model each having an identical network structure with respective layer of the target model; based on the parameter correspondences, replacing the corresponding parameters of the target model one by one with the original model parameters, validating the replaced target model with the preset validation set, and when the validation is passed, recording that the corresponding original model parameter is reusable; and using all reusable original model parameters to replace the corresponding parameters in the target model to obtain and train a new target model.