Neural Network Training with Human-Intuitive Inputs
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Solution Overview
Problem
Training neural network models for assets in extended reality environments is a tedious task due to the need for manual classification and weighting of training data, which hinders the improvement of the XR experience.
Innovation Solution
Implementing a system that allows training of neural network models using human-intuitive inputs such as text, speech, or video, enabling users to select and weight training focuses through natural language or video analysis, thereby automating the generation of training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification and weighting of training data is used, then training accuracy can be improved, but training time and complexity increase significantly
Solution Approach 1:
The system automatically performs data classification and weighting without requiring manual user intervention. The neural network model self-adjusts weights based on interaction data, eliminating the need for tedious manual training data preparation while maintaining training accuracy.
Solution Approach 2:
The patent replaces manual mechanical classification processes with automated computational algorithms. Machine learning algorithms automatically analyze and weight training data based on relevance to XR asset states, substituting human manual work with intelligent automated systems.
2Measurement precision
If manual classification and weighting of training data is used, then training precision can be improved, but operational complexity increases
Solution Approach 1:
The system performs automatic data weighting and classification without requiring user expertise in machine learning processes. Users simply provide interaction data, and the system handles all complex training data preparation automatically, greatly simplifying operation.
Solution Approach 2:
The patent introduces an intermediary automated processing layer between raw interaction data and the neural network model. This intermediary system handles complex data weighting and classification tasks, shielding users from technical complexity while ensuring high training precision.
3Measurement precision
If comprehensive training data is collected, then model accuracy improves, but data processing complexity increases
Solution Approach 1:
The system applies different weighting factors to different portions of training data based on their relevance and quality. Instead of treating all data uniformly, the patent selectively weights specific data points or features, reducing processing complexity while maintaining model accuracy.
Solution Approach 2:
The patent dynamically adjusts weighting parameters based on data characteristics and model performance. By changing weight parameters automatically during training, the system handles comprehensive data efficiently without requiring complex manual processing of each data point.
Data Source
AI summary
In one implementation, a method of generating environment states is performed by a device including one or more processors and non-transitory memory. The method includes displaying an environment including an asset associated with a neural network model and having a plurality of asset states. The method includes receiving a user input indicative of a training request. The method includes selecting, based on the user input, a training focus indicating one or more of the plurality of asset states. The method includes generating a set of training data including a plurality of training instances weighted according to the training focus. The method includes training the neural network model on the set of training data.