Dynamic Weight Determination for Multi-Task Neural Network Training
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Solution Overview
Problem
Existing methods for training neural networks in multi-task learning settings often rely on fixed or randomly drawn weights for combining loss values, which can limit the prediction performance and adaptability of the neural networks.
Innovation Solution
The method involves using a second neural network to determine the weights for combining loss values, allowing these weights to be dynamically adapted based on the specific characteristics of the training data, thereby improving the classification and regression performance of the first neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed or randomly drawn weights are used for combining loss values, then the training process is simple and stable, but the prediction performance and adaptability of the neural network are limited
Solution Approach 1:
The patent transforms the static weight combination approach into a dynamic one by introducing a second neural network that learns optimal weights during training. The weights are no longer fixed or randomly drawn but are dynamically adapted based on the specific characteristics of the training data and tasks, thereby improving prediction performance while managing complexity through structured learning
Solution Approach 2:
The patent introduces a second neural network as an intermediary component that acts as a weight determination mechanism. This intermediary network processes information from the first neural network and task characteristics to generate optimized weights, mediating between the raw loss values and the final training objective function
2Adaptability or versatility
If weights are determined by a second neural network, then adaptability and prediction performance improve, but the device complexity and training difficulty increase
Solution Approach 1:
The second neural network serves multiple functions: it determines weights for loss combination, adapts to different task characteristics, and improves performance across various multi-task learning scenarios. This multi-functionality justifies the added complexity by providing broad adaptability benefits
Solution Approach 2:
The patent implements a nested architecture where the second neural network (weight determination network) is embedded within the overall training system of the first neural network. The networks are trained together in an integrated manner, with the second network nested within the training loop of the first, allowing coordinated optimization
3Productivity
If dynamic weight determination is implemented, then convergence speed improves, but the training stability may be affected
Solution Approach 1:
The patent implements feedback mechanisms where the second neural network continuously adjusts weights based on training progress and performance metrics. This feedback loop allows the system to adapt dynamically during training, improving convergence while maintaining stability through controlled adjustments rather than arbitrary changes
Data Source
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AI summary
Computer-implemented method for training a first neural network (60) comprising the steps of: • Receiving a training input signal (xi), wherein the training input signal (xi) comprises or is part of a signal (5) obtained from a sensor (30); • Determining a plurality (yi) of training output signals ( yi1,yi2,yin) by providing the training input signal (xi) as input to the fist neural network (60), wherein each training output signal ( yi1,yi2,yin) from the plurality (yi) of training output signals ( yi1,yi2,yin) characterizes a classification and/or a regression result of the training input signal (xi); • Determining a plurality of loss values ( L1,L2,Ln), wherein each loss value ( L1,L2,Ln) corresponds to a distinct training output signal ( yi1,yi2,yin) and characterizes a deviation of the training output signal ( yi1,yi2,yin) from a desired training output signal ( ti1,ti2,tin); • Determining a weighted sum ( ) of the loss values ( L1,L2,Ln), wherein the weights (σ(1), σ(2), σ(n)) for the loss values ( L1,L2,Ln) are determined by a second neural network (61); • Training the first neural network (60) by updating parameters (Φ) of the first neural network (60) according to a gradient of the sum ( ) with respect to the parameters (Φ) of the first neural network (60) and by updating parameters of the second neural network (61) according to a gradient of the sum ( ) with respect the parameters of the second neural network (61).