Summary of Fast Adaptation with Kernel and Gradient Based Meta Leaning, by Juneyoung Park and Minjae Kang
Fast Adaptation with Kernel and Gradient based Meta Leaning
by JuneYoung Park, MinJae Kang
First submitted to arxiv on: 1 Nov 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 proposed paper improves Model Agnostic Meta Learning (MAML) for few-shot learning by modifying both the inner and outer loops. The first algorithm updates the model using closed-form solutions instead of gradient steps, while the second adjusts the learning of the meta-learner by assigning weights to losses from each task. This leads to improved convergence during training and inference stages. The research presents a new perspective on meta-learning and offers a more efficient approach to few-shot learning and fast task adaptation compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes MAML better for learning with just a few examples. It fixes two problems with MAML: instability and slow computation times during training and when using the model. The first fix changes how the model is updated, and the second adjusts how the meta-learner learns from each task. This helps make MAML more reliable and efficient. |
Keywords
» Artificial intelligence » Few shot » Inference » Meta learning
