3D Content Rendering with Dynamic Small-Model Selection
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Solution Overview
Problem
Large deep neural networks face challenges such as high memory footprint, resource consumption, and latency issues, making them unsuitable for real-time operations, despite their ability to achieve high accuracy for general datasets.
Innovation Solution
Implementing a system that uses multiple smaller machine-learning models, each trained for specific datasets or conditions, and dynamically selects the most efficient model based on accuracy and loss comparisons to process input data, reducing resource usage and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large deep neural network is used to achieve high accuracy, then the accuracy is improved, but the memory footprint and processing resources increase
Solution Approach 1:
The patent divides a large deep neural network into multiple smaller deep neural networks, each specialized for specific datasets or conditions. This segmentation allows the system to maintain high accuracy for specific tasks while reducing the memory footprint and processing resources required compared to using a single large network for all tasks.
Solution Approach 2:
The patent creates a universal system that can handle multiple different datasets and conditions using a collection of smaller specialized networks. The system selects the appropriate network based on the input data characteristics, making the overall system versatile while keeping individual network components small and efficient.
2Measurement precision
If a large deep neural network is used to achieve high accuracy, then the accuracy is improved, but the processing resources and latency increase
Solution Approach 1:
The patent segments the large network into smaller specialized networks, reducing the processing power required for each inference. Each small network is optimized for specific data types, allowing faster processing with fewer computational resources while maintaining high accuracy for those specific tasks.
Solution Approach 2:
The patent implements a dynamic selection mechanism that chooses the appropriate small network based on the characteristics of the input data. This dynamic approach ensures that only the necessary computational resources are activated for each specific task, reducing overall processing power consumption and latency.
3Measurement precision
If a large deep neural network is used to achieve high accuracy, then the accuracy is improved, but the training time increases
Solution Approach 1:
The patent divides the training process into multiple smaller tasks, each training a specialized small network on specific datasets. This segmentation allows for more efficient parallel training of smaller models compared to training one large model, reducing overall training time while achieving comparable or better accuracy for specific tasks.
Solution Approach 2:
The patent trains multiple small networks on specific subsets of data rather than training one large network on all data. This partial action approach allows for faster, more focused training on specific tasks, reducing total training time while maintaining high accuracy for each specialized network.
4Measurement precision
If a large deep neural network is used to achieve high accuracy, then the accuracy is improved, but the model size increases
Solution Approach 1:
The patent segments the large model into multiple smaller specialized models, reducing the size of each individual model while maintaining high accuracy for specific tasks. The overall system complexity is managed through a selection mechanism rather than deploying one large complex model.
Solution Approach 2:
The patent uses multiple small, lightweight models that can be quickly selected and discarded based on the input data characteristics, rather than maintaining one large persistent model. This approach reduces the memory footprint and deployment complexity while achieving high accuracy when the appropriate small model is selected.
Data Source
AI summary
A system and a method for presenting three-dimensional content and a three-dimensional content calculation apparatus are provided. In the method, the calculation apparatus receives a request for presentation content including one or more images from a client device, receives the presentation content from a content delivery network according to the request, processes the images using a first machine-learning model to generate a first predicted result, processes the images using multiple machine-learning models to generate at least a second predicted result and a third predicted result, selects a second machine-learning model from the machine-learning models based on a comparison of the first predicted result with the at least the second predicted result and the third predicted result, processes the images using the second machine-learning model and sends a processing result to the client device. Accordingly, the client device generates a three-dimensional presentation of the presentation content.