MT2ST: Adaptive Multi-Task to Single-Task Learning
- URL: http://arxiv.org/abs/2406.18038v6
- Date: Thu, 01 May 2025 03:27:16 GMT
- Title: MT2ST: Adaptive Multi-Task to Single-Task Learning
- Authors: Dong Liu, Yanxuan Yu,
- Abstract summary: We address the trade-off between generalization in multi-task learning (MTL) and precision in single-task learning (STL) by introducing the Multi-Task to Single-Task (MT2ST) framework.<n>MT2ST is designed to enhance training efficiency and accuracy in multi-modal tasks, showcasing its value as a practical application of efficient machine learning.
- Score: 6.185573921868495
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Efficient machine learning (ML) has become increasingly important as models grow larger and data volumes expand. In this work, we address the trade-off between generalization in multi-task learning (MTL) and precision in single-task learning (STL) by introducing the Multi-Task to Single-Task (MT2ST) framework. MT2ST is designed to enhance training efficiency and accuracy in multi-modal tasks, showcasing its value as a practical application of efficient ML.
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