Single Neural Network Multi-Domain Adaptation Without Overfitting
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Solution Overview
Problem
Conventional deep learning methods are limited to a single domain and require additional labeled data for adaptation to new domains, leading to overfitting and reduced scalability when handling data from multiple domains.
Innovation Solution
A computer system and method that regularizes data sets from multiple domains using information theory to extract shared information, implementing a training model based on this shared information without overfitting, using a single neural network, and reinforcing feature data complexity through algorithms like batch spectral penalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a model is trained through specific domain data, then the model achieves good performance in that domain, but the model becomes overfitted and cannot be directly used in another domain
Solution Approach 1:
The patent applies domain adaptation techniques to make a single neural network model universally applicable across multiple domains. By using labeled data from source domains and unlabeled data from target domains, the model learns domain-invariant features that enable it to generalize across different domains without requiring domain-specific retraining, thus achieving both good performance and broad adaptability
Solution Approach 2:
The patent modifies the training parameters and objective functions of the neural network to accommodate multiple domains. By changing the loss function to include domain adaptation terms and adjusting training parameters during the adaptation process, the model can transition from being domain-specific to being domain-agnostic while maintaining performance
2Measurement precision
If domain adaptation methodology is used to improve performance in target domain, then model performance improves, but scalability is greatly reduced
Solution Approach 1:
The patent merges the training process for multiple target domains into a single unified framework. By simultaneously processing unlabeled data from multiple target domains along with labeled source domain data in one training run, the method achieves scalability while maintaining performance improvement across all target domains, eliminating the need for separate adaptation processes
3Quantity of substance
If data is simultaneously collected from several domains, then more diverse data is available, but information that is available in common cannot be extracted from domains at a time
Solution Approach 1:
The patent introduces an intermediary mechanism in the neural network that identifies and extracts shared information patterns across multiple domains. This intermediary layer or mechanism acts as a bridge that processes diverse data from different domains and isolates the common information that can be universally applied, preventing loss of shared information while handling data diversity
Data Source
AI summary
Various embodiments relate to a computer system for multi-source domain adaptative training based on a single neural network without overfitting and a method thereof. The various embodiments may configured to regularize data sets of a plurality of domains, extract information shared between the regularized data sets, and implement a training model by performing training based on the extracted information.