Summary of Task Weighting Through Gradient Projection For Multitask Learning, by Christian Bohn et al.
Task Weighting through Gradient Projection for Multitask Learning
by Christian Bohn, Ido Freeman, Hasan Tercan, Tobias Meisen
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 paper proposes an adaptation of the PCGrad algorithm, which typically improves training performance by addressing conflicts between task gradients in multitask learning. The new method incorporates task prioritization by introducing a probability distribution that determines when and how task gradients are projected. This approach differs from traditional task weighting schemes, allowing for unhindered training in non-conflict situations. Experiments on nuScenes, CIFAR-100, and CelebA datasets demonstrate the effectiveness of this method in improving performance metrics for most tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to help machines learn multiple tasks at once. When different tasks have conflicting goals, it can be tricky to decide which one to prioritize. The authors suggest a solution that uses probabilities to determine when and how to focus on each task. This approach works well with various datasets and shows significant improvements in performance. |
Keywords
» Artificial intelligence » Probability
