Multi-Task Model Training with Shared and Task-Specific Sub-Models
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Solution Overview
Problem
Existing neural network models require separate training for each task, which is costly and challenging due to the need for unified model input and output, making it difficult to train a multi-task model with different datasets for various tasks.
Innovation Solution
A multi-task model comprising a shared sub-model and dedicated sub-models for multiple tasks, trained using a method that determines task trigger states based on association information, obtaining and utilizing task-specific training data to adjust model parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate training is performed for each task using existing neural network models, then task-specific performance is maintained, but training cost and complexity increase significantly
Solution Approach 1:
The model is segmented into a shared sub-model and task-specific sub-models. The shared sub-model processes common features across multiple tasks, while task-specific sub-models handle individual task requirements. This segmentation allows training to be performed more efficiently by sharing common processing logic while maintaining task-specific capabilities through dedicated sub-models.
Solution Approach 2:
The shared sub-model is designed to be universal and can process inputs for multiple different tasks simultaneously. By training the shared sub-model on multi-task data that encompasses various task types, the model learns generalizable features that can be applied across different tasks, reducing the need for separate specialized training for each task.
2Measurement precision
If separate training is performed for each task, then task accuracy is maintained, but training time and resource consumption increase
Solution Approach 1:
The training process continuously updates the shared sub-model with multi-task data while simultaneously training task-specific sub-models. This continuous training approach allows the model to learn from multiple tasks concurrently, maintaining task accuracy while improving training efficiency through parallel learning processes rather than sequential training.
Solution Approach 2:
The shared sub-model is trained preliminarily on multi-task data before final task-specific training. This preliminary training establishes a strong foundation of generalizable features that accelerates subsequent task-specific training, reducing overall training time while maintaining or improving final task accuracy.
3Device complexity
If a unified model structure is used for multiple tasks, then training cost is reduced, but the difficulty of handling different datasets and task requirements increases
Solution Approach 1:
The unified model is segmented into a shared sub-model and task-specific sub-models. This segmentation allows the model to maintain a relatively simple overall structure while accommodating different task requirements through dedicated sub-models that can be independently configured and trained for specific task characteristics.
Solution Approach 2:
The model structure is made dynamic by allowing different task-specific sub-models to be activated or deactivated based on the input task requirements. This dynamic configuration enables the unified model to adapt to different task types and dataset characteristics without requiring a completely different architecture for each task.
Data Source
AI summary
Embodiments of the present disclosure provide a method, an electronic device, a computer-readable storage medium, and a computer program product for training a multi-task model. The multi-task model includes a shared sub-model and a plurality of dedicated sub-models corresponding to a plurality of tasks respectively, and the method includes: performing operations for each of the plurality of tasks respectively: determining a trigger state of the task based on association information of the task; in response to the trigger state indicating that the task is triggered for training the multi-task model, obtaining a set of training data corresponding to the task; and training the shared sub-model and a dedicated sub-model corresponding to the task with the set of training data corresponding to the task.