Unified Machine Learning Model with Conditional Task Execution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models, such as deep neural networks, are typically configured to perform a single task, leading to inefficiencies when multiple processing tasks need to be performed using separate networks, resulting in redundant operations and increased memory footprint due to serial processing.
Innovation Solution
A method where a trained machine learning model with a common part and task-specific parts processes input data by executing only the necessary parts based on an execution instruction, allowing for conditional execution and parallel processing of multiple tasks using a single set of input data, thereby reducing redundancy and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple separate neural networks are used to perform multiple processing tasks, then each task can be handled independently, but the memory footprint and device complexity increase due to redundant operations
Solution Approach 1:
The patent merges multiple separate neural networks into a single unified model that shares common processing components (encoder, feature extraction layers) while maintaining task-specific output heads. This combining approach eliminates redundant operations across multiple networks, reducing memory footprint and device complexity while preserving the ability to handle multiple processing tasks simultaneously.
Solution Approach 2:
The unified neural network model is designed with universal common parts that can serve multiple different processing tasks. The shared encoder and feature extraction layers provide multi-functional capability, allowing the same model structure to perform various tasks (e.g., classification, detection, segmentation) by activating different task-specific components as needed.
2Adaptability or versatility
If three separate neural networks are used to perform three processing tasks, then each task is handled by a dedicated network, but redundant operations increase processing time and resource consumption
Solution Approach 1:
By merging multiple specialized networks into one unified model with shared common parts, the patent eliminates redundant processing operations. The shared encoder and feature extraction layers process input data once, and the results are then distributed to different task-specific output heads, significantly improving processing efficiency compared to running three separate networks independently.
Solution Approach 2:
The unified model is segmented into common parts (shared encoder, feature extraction) and task-specific parts (different output heads). This segmentation allows the system to maintain task specialization while sharing computational resources, enabling efficient parallel processing of multiple tasks without the overhead of completely separate networks.
3Productivity
If a single machine learning model performs multiple tasks, then resource utilization improves, but the model complexity and training difficulty increase
Solution Approach 1:
The model is segmented into distinct common parts and task-specific parts, making the complexity manageable. The common parts handle shared processing functionality, while task-specific parts handle individual task requirements. This modular segmentation reduces training difficulty compared to a fully integrated complex model, as each segment can be trained and optimized independently to some extent.
Data Source
AI summary
A method includes receiving input data at a trained machine learning model that includes a common part and task-specific parts, receiving an execution instruction that identifies one or more processing tasks to be performed, processing the input data using the common part of the trained machine learning model to generate intermediate data, and processing the intermediate data using one or more of the task-specific parts of the trained machine learning model based on the execution instruction to generate one or more outputs.