Multi-task Neural Network Training via Combined Loss and Control Parameters
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Solution Overview
Problem
Multi-task neural networks are difficult to train due to the need to capture different levels of information for various tasks, and the black box nature of deep neural networks makes it hard to understand what features are being extracted for each task.
Innovation Solution
A computer-implemented method for training a multi-task neural network that uses a combined loss function to minimize the loss across multiple tasks, incorporating a regularization term based on the difference in distance metric values between tasks, and utilizing an optimization neural network to predict single-task control parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multi-task neural networks are trained to perform multiple tasks simultaneously, then the model can capture different levels of information for various tasks, but the training becomes difficult due to the black box nature of deep neural networks making it hard to understand what features are being extracted
Solution Approach 1:
The patent segments the training process by introducing task-specific control parameters that separately regulate the training progress of each task. This segmentation allows independent monitoring and control of feature extraction for each task, making the black box more transparent while maintaining multi-task capability
Solution Approach 2:
The patent introduces distance metric values and control parameters as intermediary elements between the neural network's internal feature extraction process and the external training objective. These intermediaries provide measurable indicators of training progress and feature extraction quality for each task
2Measurement precision
If different tasks require capturing different levels of information, then each task can be optimized for its specific needs, but the training speeds of tasks with different complexity levels become misaligned
Solution Approach 1:
The patent implements dynamic control of training speeds by introducing task-specific control parameters that are updated based on distance metric values. This allows the training process to adaptively adjust the progress of each task, ensuring that tasks with different complexity levels converge at appropriate rates
Solution Approach 2:
The patent establishes a feedback mechanism where distance metric values computed from task performance inform the adjustment of control parameters. This feedback loop enables automatic alignment of training speeds without manual intervention, balancing the information capture needs of different tasks
3Productivity
If a combined loss function is used to train multiple tasks simultaneously, then the model can learn from all tasks together, but it is hard to know what features or information the model must extract from the input data for each task
Solution Approach 1:
The patent applies local quality by introducing task-specific control parameters that differentially regulate the training objective for each task within the combined loss function. This allows the model to maintain task-specific feature extraction characteristics while benefiting from joint training, preventing the loss of task-specific information
Data Source
AI summary
A computer-implemented method for training a multi-task neural network for predicting a plurality of T tasks, T≥2, simultaneously based on input data, the method comprising: a) providing a multi-task neural network, a training dataset for training the neural network on the plurality of T tasks, and a validation dataset ′ for validating the neural network on the plurality of T tasks; and b) training the multi-task neural network for the plurality of T tasks across a predefined number Nepoch of training epochs by using the training dataset and the validation dataset ′ such that a combined loss function is minimized within the predefined number Nepoch of training epochs.