Neural Network Training for Fast Adaptation to New Sensor Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Training artificial neural networks for processing digital sensor data requires significant computational resources and time, especially when adapting to new sensors or applications.
Innovation Solution
A two-phase training method using a differentiable architecture search (DARTS) and meta-learning (MAML) algorithms to optimize the architecture and weights of neural networks, allowing for rapid adaptation to new sensor data with minimal additional training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional training methods are used to adapt neural networks to new sensors or applications, then the network can be trained to perform the specific task, but significant computational resources and training time are required
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on a diverse distribution of training tasks before deployment. This pre-training phase prepares the network to quickly adapt to new sensors or applications with minimal additional training data, thereby reducing the training time required when adaptation is needed.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting the architecture parameters and weights of the neural network based on the specific sensor or application being adapted to. The system modifies network parameters efficiently during the adaptation phase, enabling quick customization without requiring extensive retraining.
2Adaptability or versatility
If traditional training methods are used to adapt neural networks to new sensors or applications, then the network can be trained to perform the specific task, but significant computational resources are required
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on a diverse distribution of training tasks before deployment. This pre-training phase prepares the network to quickly adapt to new sensors or applications with minimal additional training data, thereby reducing the training time required when adaptation is needed.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting the architecture parameters and weights of the neural network based on the specific sensor or application being adapted to. The system modifies network parameters efficiently during the adaptation phase, enabling quick customization without requiring extensive retraining.
3Reliability
If a neural network is trained extensively on one specific application, then it performs well on that application, but it requires significant training effort when adapting to a new application
Solution Approach 1:
The patent applies universality by training the neural network on a diverse distribution of multiple training tasks rather than a single application. This multi-task pre-training gives the network universal capabilities that allow it to adapt to different applications more easily, reducing the training effort required for new applications while maintaining reliable performance.
Data Source
Figure 1~2
Figure 3~4
Figure 5
AI summary
The invention relates to a device, a computer-implemented method for processing digital sensor data and a training method for same, wherein a plurality of training tasks from a range of training tasks is provided (302), wherein the training tasks characterise the processing of digital sensor data, wherein a parameter set for an architecture and for a weight of an artificial neural network is determined according to at least one first training task from the range of training tasks with a first gradient-based learning algorithm and a second gradient-based algorithm (304), wherein the artificial neural network is trained according to the parameter set and according to a second training task with the first gradient-based learning algorithm (306).