Neural Network Training with Optimizer Switching for Face Detection
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Solution Overview
Problem
Existing neural network training methods face challenges in balancing training speed and generalization ability due to the limitations of single optimization modes, which can result in slow convergence, oscillation, or poor generalization, particularly in deep learning applications.
Innovation Solution
A method involving multiple optimization modes, such as adaptive moment estimation (Adam) and stochastic gradient descent (SGD), where the learning rate is dynamically adjusted by mapping the first learning rate to a second learning rate in the same vector space, allowing for switching between optimization modes based on convergence conditions to optimize training speed and generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single optimization mode is used for neural network training, then the training process is simple, but the training speed and generalization ability cannot be balanced
Solution Approach 1:
The patent applies dynamics by making the optimization mode adjustable and switchable during training. The system dynamically selects between first optimization mode (for training speed) and second optimization mode (for generalization ability) based on real-time monitoring of training metrics, allowing the training process to adapt its characteristics rather than being fixed
Solution Approach 2:
The patent changes the parameter of optimization mode from a static single-mode configuration to a dynamic multi-mode configuration. By introducing multiple optimization modes and switching between them based on training state, the system transforms the training process to achieve both fast convergence and good generalization
2Reliability
If the learning rate is too small, then the generalization ability is maintained, but the training speed becomes slow
Solution Approach 1:
The learning rate is made dynamic through the switching mechanism. During early training stages, a larger learning rate is used to achieve fast convergence. When the model approaches optimal performance, the system switches to a smaller learning rate to maintain stability and generalization ability, thus achieving both fast training and good generalization
3Productivity
If the learning rate is too large, then the training speed is fast, but the generalization ability deteriorates
Solution Approach 1:
The system dynamically adjusts the effective learning rate by switching optimization modes. The first optimization mode uses a larger learning rate for fast initial training, while the second optimization mode uses a smaller learning rate for fine-tuning. The switching mechanism ensures that the large learning rate is only used when appropriate, preventing generalization deterioration
4Productivity
If multiple optimization modes are used, then training efficiency is improved, but the system complexity increases
Solution Approach 1:
The patent uses feedback to manage the complexity of multiple optimization modes. The system monitors training metrics (such as loss value changes and convergence state) and uses this feedback to automatically switch between optimization modes. This feedback-driven approach simplifies the user's task by automating the selection process, making the multi-mode system as easy to use as a single-mode system while achieving better training efficiency
Data Source
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AI summary
Disclosed are a neural network training and method for detecting a face and apparatus, and a device and a storage medium. The training method comprises: determining a neural network; training the neural network at a first learning rate according to a first optimization mode, the first learning rate being updated when the neural network is trained each time; mapping the first learning rate of the first optimization mode into a second learning rate of a second optimization mode in the same vector space; determining that the second learning rate satisfies a preset update condition; and continuing to train the neural network at the second learning rate according to a second optimization mode.