Multi-task Gating for Neural Network Feature Allocation
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Solution Overview
Problem
Conventional multi-task machine learning systems face inefficiencies and accuracy issues due to redundant computation and interference between tasks, particularly in edge-device applications with limited processing bandwidth, where tasks with different difficulty levels and loss scales can lead to suboptimal training dynamics and feature sharing challenges.
Innovation Solution
Implementing a gating mechanism in convolutional neural networks to dynamically learn optimal feature sharing and segregation schemes, allowing each layer to determine whether to process shared or task-specific features, thereby adapting the network's capacity and optimizing feature allocation across tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional multi-task machine learning systems process all tasks through shared features, then model complexity is reduced, but task interference increases and accuracy deteriorates
Solution Approach 1:
The patent segments the feature processing path into shared and task-specific branches with gating mechanisms. Each gate independently controls whether to use shared or task-specific features for each task, allowing the system to segment feature usage based on task requirements rather than forcing all tasks through a single shared path.
Solution Approach 2:
The patent introduces dynamic gating mechanisms that adaptively switch between shared and task-specific features during training and inference. The gates are trained to dynamically determine the optimal feature source for each task based on current input and task requirements, making the system flexible rather than static.
2Reliability
If task-specific features are used for all tasks, then task performance accuracy is improved, but computational redundancy increases and processing efficiency deteriorates
Solution Approach 1:
The patent merges shared feature processing and task-specific feature processing into a unified architecture with gating mechanisms. The gates combine information from both shared and task-specific branches, allowing the system to leverage commonalities across tasks while maintaining task-specific capabilities when needed.
Solution Approach 2:
The shared branch serves multiple tasks universally by extracting common features that can be reused across different tasks. The gating mechanism enables this universal feature extraction to benefit multiple tasks simultaneously, improving processing efficiency for tasks that share common patterns.
3Ease of operation
If all tasks share the same loss scale, then training simplicity is maintained, but training dynamics deteriorate due to tasks with different difficulty levels
Solution Approach 1:
The patent applies local quality by allowing different loss scales for different tasks within the overall training framework. Each task can have its own loss scale parameter that is independently optimized, enabling difficult tasks to contribute more to gradient updates while easy tasks provide stable baseline signals, improving overall training dynamics.
4Stability of the object's composition
If fixed feature allocation is used across tasks, then system stability is maintained, but adaptability to different task configurations deteriorates
Solution Approach 1:
The patent replaces fixed feature allocation with dynamic gating mechanisms that adapt to different task configurations. The gates are trained to automatically determine the optimal feature allocation for each task based on the specific requirements and characteristics of the tasks being performed, enabling the system to adapt to various task configurations.
Solution Approach 2:
The system performs self-service by automatically determining optimal feature allocation through the trained gating mechanisms without requiring manual configuration. The gates learn to self-adjust feature routing based on task performance feedback, enabling the system to adapt to new task configurations autonomously.
Data Source
AI summary
Systems and techniques are described herein for training and using multitask machine learning models. For example, a computing device can obtain training data for a first task in a layer in a neural network; perform, based on a determination from a first gating mechanism, the shared function on shared features of the training data using at least one shared channel to generate a shared feature map; perform, based on the determination from the first gating mechanism, the first task-specific function on first task-specific features of the training data using at least one first task-specific channel to generate a first task-specific feature map; generate an output for the first task-specific branch based on performing the shared function on the shared features and performing the first task-specific function on the first task-specific features; and update at least one parameter of the first gating mechanism based on the output.