Summary of Multi-task Learning with Multi-task Optimization, by Lu Bai et al.
Multi-Task Learning with Multi-Task Optimization
by Lu Bai, Abhishek Gupta, Yew-Soon Ong
First submitted to arxiv on: 24 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed multi-task learning approach solves multiple correlated tasks simultaneously while optimizing different trade-offs in a single algorithmic pass. This is achieved by casting multi-task learning as a multi-objective optimization problem and decomposing it into unconstrained scalar-valued subproblems, which are then solved jointly using a novel multi-task gradient descent method. The method’s uniqueness lies in the iterative transfer of model parameters among subproblems during optimization, allowing for faster convergence. Experimental results on various problem settings, including image classification and scene understanding, demonstrate significant advancements over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us solve multiple problems at once, which is useful when different tasks are related to each other. Instead of trying to find the best solution for one task, we can optimize all tasks together and get a set of good solutions that balance different priorities. The new method works by breaking down the problem into smaller parts, solving each part separately, and then combining the results. This allows it to converge faster than previous methods. We tested this approach on images and other data and found it outperformed current best practices. |
Keywords
» Artificial intelligence » Gradient descent » Image classification » Multi task » Optimization » Scene understanding
