Deep Labeling System Using Transfer Learning for Contextual Data
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Solution Overview
Problem
Developing contextually intelligent apparatus is challenging due to inadequate manual labeling of geographic data and the requirement for large datasets for training deep neural networks, which often are not available.
Innovation Solution
The system employs pretrained deep learning models, adapts them for target domains through transfer learning, and combines outputs to provide ensemble labels for image data, addressing the limitations of manual labeling and dataset size with a crowd-assisted approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of geographic data is used, then human expertise can be applied, but the labeling process becomes inadequate due to the sheer number of locations and limited accuracy
Solution Approach 1:
The patent uses pretrained deep learning models trained on large source domain datasets to generate labels for target domain locations. Instead of manually labeling each location, the system copies labeling patterns and features from the source domain models to automatically generate labels for target locations, achieving both high accuracy and high throughput
Solution Approach 2:
The system performs domain adaptation by adjusting model parameters when transitioning from source domain to target domain. The deep learning models are fine-tuned with target domain data, changing their parameters to better fit the specific characteristics of target locations while maintaining the benefits of pretraining on large datasets
2Measurement precision
If deep neural networks are trained on very large datasets, then model accuracy improves, but suitable datasets of sufficient size are often not available for many applications
Solution Approach 1:
The patent pretrains deep learning models on large source domain datasets before deploying them to the target domain. This preliminary training action allows the models to learn general features and patterns from abundant source data, which can then be adapted to target domains with limited data availability
Solution Approach 2:
The deep learning models are designed to be universal across multiple domains. By training on diverse source domain data and implementing domain adaptation mechanisms, the models can function effectively in multiple target domains, reducing the need for separate large datasets for each application
3Measurement precision
If pretrained deep learning models are adapted for target domains through transfer learning, then labeling accuracy for target domains improves, but the system complexity increases
Solution Approach 1:
The system segments the labeling process into distinct phases: pretraining on source domain, domain adaptation for target domain, and ensemble combination. This segmentation allows each component to be optimized independently while maintaining overall system manageability and clarity
4Reliability
If multiple pretrained deep learning models are used to provide ensemble outputs, then labeling reliability improves, but computational resources and processing time increase
Solution Approach 1:
The system uses an ensemble of pretrained models but applies selective adaptation - not all models are fully adapted to the target domain. Some models retain their source domain training while others undergo partial adaptation, reducing computational overhead while maintaining reliability through diverse model predictions
Data Source
AI summary
An apparatus for contextual execution comprises a processor, and a memory containing instructions, which when executed by the processor, cause the apparatus to receive, from a user terminal, a control input associated with an intent, obtain location data associated with a location of the user terminal, and determine a scored set of execution options associated with the control input. Further, the instructions, when executed by the processor cause the apparatus to obtain a contextual label associated with the location data, the label determined based on the application of one or more adapted pretrained deep learning models to the location data.