Summary of Task Addition in Multi-task Learning by Geometrical Alignment, By Soorin Yim et al.
Task Addition in Multi-Task Learning by Geometrical Alignment
by Soorin Yim, Dae-Woong Jeong, Sung Moon Ko, Sumin Lee, Hyunseung Kim, Chanhui Lee, Sehui Han
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces a novel algorithm called Geometrically Aligned Transfer Encoder (GATE) that uses soft parameter sharing to transfer knowledge from abundant datasets to those with scarce data. However, GATE faces limitations in scaling to multiple tasks due to computational costs. The authors propose a task addition approach for GATE to improve performance on target tasks with limited data while minimizing computational complexity. This is achieved through supervised multi-task pre-training on a large dataset and the addition of task-specific modules for each target task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps solve the problem of training deep learning models on limited data by using an algorithm called Geometrically Aligned Transfer Encoder (GATE). GATE takes knowledge from big datasets and applies it to smaller ones. The authors make GATE better by adding more tasks to learn about, which helps with performance and is efficient. |
Keywords
» Artificial intelligence » Deep learning » Encoder » Multi task » Supervised
