Deep Learning Model Training Synchronization
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Solution Overview
Problem
Existing deep learning models are trained separately, leading to low training efficiency and limited ability to adapt to different scenarios and performance requirements.
Innovation Solution
A method where a first deep learning model with more parameters is trained alongside a second deep learning model with fewer parameters, synchronously adjusting parameter values based on a target loss to enhance training efficiency and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep learning models are trained separately, then each model can be optimized independently, but training efficiency is low and adaptability to different scenarios is limited
Solution Approach 1:
The patent combines multiple deep learning models with different parameter scales into a unified training framework. The system maintains a first model with more parameters and a second model with fewer parameters, training them simultaneously with shared parameter updates. This merging approach enables the models to adapt to different scenarios while improving training efficiency through coordinated optimization.
Solution Approach 2:
The patent segments the model parameters into two groups: a first set of parameters for the larger model and a second set of parameters for the smaller model. During training, the system selectively updates parameters from both groups based on loss function evaluations, allowing independent optimization of different parameter subsets while maintaining overall model adaptability.
2Adaptability or versatility
If a single deep learning model is used, then the system is simple to manage, but it cannot efficiently adapt to different performance requirements and scenarios
Solution Approach 1:
The patent creates a universal training framework that handles multiple models with different parameter scales. The system uses a unified loss function and parameter update mechanism that works across both the first model (more parameters) and second model (fewer parameters), enabling multi-functionality without proportionally increasing management complexity.
Solution Approach 2:
The patent implements dynamic parameter selection during training. The system evaluates loss functions for both models and selectively updates parameters based on performance requirements. This dynamic approach allows the system to adapt to different scenarios by activating appropriate parameter subsets, reducing the perceived complexity while maintaining versatility.
3Adaptability or versatility
If multiple models with different parameter counts are trained, then adaptability improves, but the training process becomes more complex
Solution Approach 1:
The patent applies local quality by treating different parameter subsets differently during training. The first parameters (for the larger model) and second parameters (for the smaller model) are updated based on their respective loss function evaluations. This localized parameter treatment enables adaptability while managing complexity through targeted updates rather than uniform processing of all parameters.
Data Source
AI summary
A training method, an inference method, a device, an apparatus, and a medium for a deep learning model are provided. A first model includes a plurality of first parameters, a second model comprises a plurality of second parameters, which is initialized to parameter values of a plurality of target parameters selected from the plurality of first parameters. The training method includes: determining a target loss for both the first model and the second model; adjusting parameter values, including: in response to determining that the target loss indicates that the parameter values of at least part of the target parameters need to be adjusted, synchronously adjusting the parameter values of the corresponding second parameters; and in response to determining that the target loss indicates that the parameter values of at least part of the second parameters need to be adjusted, synchronously adjusting the parameter values of the corresponding target parameters.