Neural Network Partial Model Training with Variable Dropout Rates
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Solution Overview
Problem
Current neural network training methods, such as dropout before the softmax layer, lack flexibility and do not adequately account for the model's structure, limiting the generation of accurate models.
Innovation Solution
An information processing method that trains a model with a first partial model and a second partial model using different dropout rates, allowing for more flexible training based on the model's structure, and adjusts the size of the hidden layer according to the dropout rate to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a single dropout rate is used for training the entire model, then the training process is simple, but the model accuracy is limited and does not account for different structural requirements of different partial models
Solution Approach 1:
The model is divided into multiple partial models (first partial model, second partial model, etc.), each trained with different dropout rates. This segmentation allows each partial model to receive customized training parameters suitable for its specific structural characteristics, thereby improving overall model accuracy without applying a one-size-fits-all approach.
Solution Approach 2:
Different dropout rates are assigned to different partial models based on their specific structural requirements. This local quality principle ensures that each part of the model receives the appropriate training intensity and regularization strength tailored to its function, rather than applying uniform training parameters across the entire model.
2Reliability
If the dropout rate is increased to improve model robustness, then overfitting is reduced, but the training convergence speed decreases
Solution Approach 1:
The dropout rate is made dynamic and adaptive rather than fixed. Different partial models use different dropout rates that can be adjusted based on their specific needs, allowing the training process to balance robustness and convergence speed optimally for each component of the model.
Solution Approach 2:
The dropout rate parameter is changed and optimized for different partial models. By adjusting this critical training parameter locally rather than globally, the system achieves better robustness where needed while maintaining faster convergence in other parts of the model.
Data Source
AI summary
An information processing method according to the present application is an information processing method executed by a computer, the information processing method including: acquiring learning data used for training of a model including a first partial model and a second partial model; and generating, by using the learning data, the model in a manner in which the first partial model is trained by first dropout based on a first dropout rate and the second partial model is trained by second dropout based on a second dropout rate different from the first dropout rate.