Multi-Task Learning via Task Similarity Analysis
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Solution Overview
Problem
Existing deep learning models face challenges in performing multiple tasks efficiently due to limited resources, particularly in on-device products where memory constraints prevent simultaneous task execution, necessitating the development of methods for efficient multi-task learning.
Innovation Solution
The method involves performing similarity analysis between tasks using image datasets, pre-training a neural network, and allocating parameters based on similarity, allowing for efficient learning of new tasks by leveraging pre-trained parameters, thereby enabling consecutive multi-task learning with limited memory resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple tasks occupy memories independently in deep learning models, then each task can be trained separately, but memory resources are insufficient for on-device products with limited capacity
Solution Approach 1:
The patent merges multiple task parameters into a single shared parameter space within one neural network. Instead of maintaining separate memory spaces for each task, the system combines them and uses similarity analysis to determine which parameters to activate, enabling multiple tasks to coexist in limited memory resources.
Solution Approach 2:
The neural network is designed to perform multiple functions (tasks) using a single unified architecture. The same network structure serves different tasks by dynamically selecting relevant parameters based on task similarity, making the system universally applicable to various tasks without requiring separate dedicated models for each.
2Productivity
If a single neural network is trained for multiple tasks simultaneously, then resource efficiency is improved, but task-specific performance may be compromised
Solution Approach 1:
The patent applies local quality by selectively activating different parameter subsets for different tasks. Instead of treating all parameters equally, the system identifies task-specific relevant parameters through similarity analysis and activates only those, allowing each task to receive focused computational resources while sharing the overall network structure.
Solution Approach 2:
The system dynamically adjusts which parameters are activated based on the current task being performed. By continuously analyzing task similarity and updating parameter selection in real-time, the neural network adapts its effective capacity to match the specific requirements of each task, maintaining high performance across diverse tasks.
3Adaptability or versatility
If similarity analysis is performed between tasks, then parameter allocation is optimized, but computational overhead is increased
Solution Approach 1:
The patent performs similarity analysis between tasks in advance during the training phase to pre-determine parameter allocation strategies. By conducting this analysis before actual inference, the system builds up task similarity relationships and pre-computes parameter mapping, reducing the computational burden during runtime when tasks need to be executed.
Data Source
AI summary
The present disclosure relates to a method and apparatus for performing multiple tasks based on task similarity by using artificial intelligence.According to an embodiment of the present disclosure, a method for performing multi-task learning based on task similarity may include performing a similarity analysis between a first task and a second task and training a neural network for the second task based on a result of the similarity analysis. Herein, wherein in response to be determined that a first training dataset used for the first task and a second training dataset used for the second task are similar, the neural network may learn a second parameter allocated to the second training dataset based on a first parameter allocated to the first training dataset.