A Model-Agnostic Approach to Mitigate Gradient Interference for Multi-Task Learning.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Deep Learning

Background:

• Multitask learning (MTL) enables joint training of multiple tasks but faces challenges with task conflicts.
• Existing methods for mitigating task conflicts require manual effort and domain expertise.
• Formal descriptions and theoretical underpinnings of task conflicts in MTL are lacking.

Purpose of the Study:

• To formally describe task conflicts in MTL as a gradient interference problem.
• To propose a novel, model-agnostic approach to mitigate gradient interference.
• To theoretically analyze the proposed method and validate its effectiveness.

Main Methods:

• Formalized task conflicts as gradient interference.
• Developed a model-agnostic approach to mitigate gradient interference (MAMG) using a gradient clipping rule.
• Theoretically proved the convergence of MAMG.

Main Results:

• MAMG effectively mitigates gradient interference in multitask learning.
• The proposed method demonstrates superiority over existing MTL techniques.
• Extensive experiments on large datasets show MAMG outperforms state-of-the-art algorithms across diverse tasks.

Conclusions:

• MAMG offers a general and effective solution for task conflicts in MTL.
• The model-agnostic nature of MAMG allows easy integration with various MTL architectures.
• This work provides theoretical insights and practical improvements for multitask learning.