Wake-Up Model Optimization via Periodic Dataset Updates
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Solution Overview
Problem
Existing wake-up models face challenges in achieving high optimization efficiency and effectiveness due to limited data for false wake-ups, leading to poor optimization efficiency and stability, and a high risk of overfitting.
Innovation Solution
The method involves periodically updating the training and verification sets based on a preset corpus database during iterative training, continuing the training process until a termination condition is met, which helps improve the wake-up model's stability and adaptability by incorporating more diverse data scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the wake-up model is optimized using a pre-collected tuning set with limited false wake-up data, then the initial model performance is achieved, but the optimization efficiency and effectiveness deteriorate due to insufficient data diversity
Solution Approach 1:
The patent performs preliminary action by pre-collecting a large-scale corpus database before the optimization phase. This corpus is then dynamically sampled during iterative training to update the training and verification sets, ensuring sufficient data diversity is available when needed without requiring extensive manual data collection during optimization.
Solution Approach 2:
The patent applies dynamics by transforming the static pre-collected tuning set into a dynamic data generation system. The training and verification sets are periodically updated during iterative training by sampling from the corpus database based on current model performance, allowing the data distribution to adapt dynamically throughout the optimization process.
2Adaptability or versatility
If the training set is updated frequently with diverse data, then the model's adaptability and stability improve, but the training time and computational resources increase
Solution Approach 1:
The patent implements periodic action by updating the training and verification sets at fixed intervals during iterative training rather than continuously. This periodic update mechanism balances the need for data diversity with training efficiency, allowing the model to stabilize on current data before introducing new samples from the corpus database.
Solution Approach 2:
The patent maintains continuity of useful action by continuously sampling from the corpus database to generate updated training data throughout the iterative process. This ensures the model consistently benefits from diverse data without interruption, while the periodic update rhythm prevents excessive computational overhead at any single stage.
Data Source
AI summary
Provided are an optimization method, apparatus, device for a wake-up model and a storage medium, which allow for: acquiring a training set and a verification set; performing an iterative training on the wake-up model according to the training set and the verification set; during the iterative training, periodically updating the training set and the verification set according to the wake-up model and a preset corpus database, and continuing performing the iterative training on the wake-up model according to the updated training set and verification set; and outputting the wake-up model when a preset termination condition is reached. The embodiments of the present disclosure, by periodically updating the training set and the verification set according to the wake-up model and the preset corpus database during an iteration, may improve optimization efficiency and effects of the wake-up model, thereby improving stability and adaptability of the wake-up model and avoiding overfitting.