Summary of Learning Representation For Multitask Learning Through Self Supervised Auxiliary Learning, by Seokwon Shin et al.
Learning Representation for Multitask learning through Self Supervised Auxiliary learning
by Seokwon Shin, Hyungrok Do, Youngdoo Son
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 a novel method called Dummy Gradient norm Regularization (DGR) to improve the quality of representations generated by shared encoders in multi-task learning. This approach aims to enhance the universality of these representations, which are then used for prediction tasks. The authors demonstrate the effectiveness of DGR on multiple benchmark datasets, showing better performance compared to existing methods. They also highlight its simplicity and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make machine learning models work better. It’s called multi-task learning, where one model learns to do many things at once. One problem with this approach is that the model doesn’t always learn good representations of the data. To fix this, the authors created a new method that helps the model generate better representations. They tested it on several datasets and showed that it works better than other methods. This could be important for making machine learning models more useful in many different areas. |
Keywords
» Artificial intelligence » Machine learning » Multi task » Regularization
