Neural Network Embedding Layer for Map Feature Analytics
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Solution Overview
Problem
Current deep learning technologies face challenges in making machine learning more resource-efficient and accurate for map and navigation services, particularly in map feature classification and analysis, due to the need for large amounts of training data and computational resources.
Innovation Solution
The development of a method that processes map data to extract features and trains a neural network to predict a semantic embedding layer, which represents the semantic relationships among map features, reducing the need for extensive training data and computational resources by providing this embedding layer as output for use in other neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deep learning methods are used for map feature classification, then model accuracy can be improved, but computational resources and training data requirements increase significantly
Solution Approach 1:
The patent pre-trains a separate embedding network on large-scale map data to generate embedding layers that capture semantic relationships among map features. This preliminary action allows the main classification network to start with pre-processed, semantically-enriched features, reducing the computational burden during actual classification tasks while maintaining or improving accuracy
Solution Approach 2:
The patent introduces an embedding layer as an intermediary component between the input map data and the classification network. This embedding layer acts as a mediator that transforms raw map features into semantically-meaningful representations, enabling the classification network to work with more informative features and thereby improve accuracy while potentially reducing the need for extensive training data
2Measurement precision
If more training data is used to improve neural network performance, then classification accuracy improves, but the time and resources required for training increase
Solution Approach 1:
The embedding network is pre-trained separately on large-scale map data in advance, extracting and storing semantic relationships in embedding layers. This preliminary action allows the main classification task to benefit from pre-processed semantic knowledge without requiring the same extensive training time, thus reducing training time while maintaining prediction accuracy
Solution Approach 2:
The patent extracts semantic relationships from large-scale map data into separate embedding layers that can be reused across different classification tasks. By taking out and reusing these pre-extracted semantic representations, the system avoids re-processing the same large datasets for each classification task, significantly reducing training time while preserving accuracy
3Adaptability or versatility
If a single neural network is trained to perform multiple map-related tasks, then versatility is improved, but model complexity and training difficulty increase
Solution Approach 1:
The patent creates a universal embedding layer that can be applied across multiple different classification tasks and neural network architectures. This embedding layer serves multiple functions by providing semantically-enriched features for various map-related classification problems, enabling versatility without requiring each task-specific model to be independently complex
Solution Approach 2:
The patent segments the machine learning system into two independent components: an embedding network that generates semantic representations and separate classification networks that perform specific tasks. This segmentation allows each component to be optimized independently, reducing overall model complexity while maintaining multi-task versatility through the shared embedding layer
Data Source
AI summary
An approach is provided for map embedded analytics. The approach, for instance, involves processing map data to extract one or more map features. The approach also involves training a first neural network to predict an embedding layer. The neural network is trained using the one or more extracted map features. The approach further involves generating the embedding layer using the first neural network. The embedding layer represents a semantic relationship among the one or more map features. The approach further involves providing the embedding layer as an output for embedding into a second neural network.