Online Knowledge Distillation for Balanced Multi-Task Vision Learning
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Solution Overview
Problem
Multi-task learning techniques in computer vision and image processing suffer from performance degradation in difficult tasks due to an undesired emphasis on easier tasks, leading to inefficiencies in storage and speed, and existing solutions fail to preserve efficiency while maintaining performance.
Innovation Solution
A multi-task neural network framework using a shared transformer backbone and task-specific heads, combined with online distillation and adaptive feature distillation, allows simultaneous training with single-task models to align feature representations and task weights, improving performance without increasing memory or computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multi-task learning uses a shared framework with large proportion of shared parameters, then storage efficiency and inference speed are improved, but performance degradation occurs on difficult tasks due to undesired emphasis on easier tasks
Solution Approach 1:
The patent segments the neural network into a shared backbone and separate task-specific branches, allowing different loss weights for different tasks. This segmentation enables difficult tasks to receive appropriate attention while maintaining the efficiency benefits of parameter sharing in the backbone network.
Solution Approach 2:
The patent dynamically adjusts task-specific loss weights based on task difficulty and performance metrics. By changing the parameter values of loss weights during training, the system can emphasize difficult tasks more while maintaining overall multi-task learning efficiency.
2Quantity of substance
If multi-task learning uses a shared framework with large proportion of shared parameters, then storage requirements are reduced, but performance gains in some tasks come at the cost of performance degradation on other tasks
Solution Approach 1:
The shared backbone network serves multiple tasks universally, reducing storage requirements. Meanwhile, task-specific branches and adaptive loss weighting ensure each task receives appropriate attention, maintaining performance balance across all tasks despite the shared architecture.
Solution Approach 2:
The system implements feedback mechanisms that monitor performance across different tasks and adjust loss weights accordingly. This feedback loop ensures that performance gains in easier tasks do not come at the cost of degradation in difficult tasks, maintaining overall performance balance.
3Productivity
If multi-task learning emphasizes easier tasks, then faster progress is achieved on those tasks, but slower progress or worse performance occurs on difficult tasks
Solution Approach 1:
The patent dynamically changes loss weight parameters based on task difficulty assessments and performance metrics. Difficult tasks receive higher weight multipliers to compensate for slower natural progress, while easier tasks maintain appropriate progress speed without compromising difficult task performance.
Data Source
AI summary
A method, system, apparatus, and non-transitory computer-readable medium for image processing using a multi-task neural network framework may be provided. The method be performed by one or more processors and may include receiving an input image, and performing an image processing task based on the input image using the multi-task neural network framework, wherein the multi-task neural network framework is trained using a combination of task specific losses, the task specific losses including a plurality of first losses associated with the multi-task neural network framework and a plurality of second losses associated with a plurality of single-task neural network models. The method may also include generating an output of the image processing task based on up sampling an output of the multi-task neural network framework.