Multitasking Model Architecture Branching Depth Optimization
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Solution Overview
Problem
Existing multitask learning architectures face destructive interactions between tasks during training, leading to inefficiencies and increased computational costs, particularly in high-dimensional areas like image processing.
Innovation Solution
A method to determine the architecture of a multitasking model by estimating pairwise affinities between tasks, assigning them to groups, and determining the optimal branching depth of a shared base network into branch networks, thereby mitigating negative interactions and reducing computational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single shared base network is used for all tasks, then model complexity is reduced and computational efficiency is improved, but destructive interactions between tasks occur during training
Solution Approach 1:
The network is segmented at a specific depth d, where the shared base network (layers 1 to d-1) is divided into multiple task-specific branch networks (layers d to L). This segmentation allows early layers to extract general features shared by all tasks while later layers specialize in task-specific features, reducing destructive interactions between tasks while maintaining computational efficiency through shared early processing.
2Reliability
If tasks are assigned to separate base networks, then destructive interactions between tasks are eliminated, but computational costs increase significantly
Solution Approach 1:
Multiple task-specific branch networks are merged into a unified architecture that shares a common base network for early layers. This merging allows the model to share computational resources and feature extraction capabilities across tasks while maintaining task-specific processing in later layers, thereby reducing overall computational costs compared to completely separate networks.
3Productivity
If the branching depth is increased, then more features are shared between tasks improving efficiency, but destructive interactions between tasks increase
Solution Approach 1:
The branching depth d is optimized by evaluating different depth values to find the optimal point that balances feature sharing and task interaction. This parameter optimization allows the system to adapt the architecture to specific task requirements, maximizing computational efficiency while minimizing destructive interactions by selecting the appropriate depth where tasks still benefit from shared features but have sufficient capacity for task-specific processing.
Data Source
AI summary
A method for determining an architecture of a multitasking model with a number of L layers Li with i=1 to L for achieving a set of at least two, in particular a plurality of, mutually different tasks Ti with i=>2. The method includes: for the set of tasks to be achieved, estimating pairwise affinities between the tasks to be achieved of the set; assigning the tasks to be achieved to a number of N groups gi with i=1 to N on the basis of the pairwise affinities; and determining a branching depth of the multitasking model with layer, wherein the branching depth specifies at what depth of the layers of the multitasking model a base network of the multitasking model that is shared by the tasks to be achieved branches into a number of N branch networks Zi with i=1 to N.