MT2ST: Adaptive Multi-Task to Single-Task Learning
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916715468685312 |
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| author | Liu, Dong Yu, Yanxuan |
| author_facet | Liu, Dong Yu, Yanxuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18038 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MT2ST: Adaptive Multi-Task to Single-Task Learning Liu, Dong Yu, Yanxuan Machine Learning 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. |
| title | MT2ST: Adaptive Multi-Task to Single-Task Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.18038 |