Summary of Cooperative Meta-learning with Gradient Augmentation, by Jongyun Shin et al.
Cooperative Meta-Learning with Gradient Augmentation
by Jongyun Shin, Seunjin Han, Jangho Kim
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 cooperative meta-learning framework called CML, which leverages gradient-level regularization with gradient augmentation. CML introduces a co-learner that doesn’t update in the inner loop but only in the outer loop, allowing it to augment gradients for finding better meta-initialization parameters without additional cost or performance degradation. This approach is applicable to gradient-based meta-learning methods and leads to increased performance in few-shot regression, image classification, and node classification tasks. The CML framework injects learnable noise into the model’s gradient to enhance generalization. Experiments demonstrate that CML outperforms MAML, a widely used gradient-based meta-learning method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for machines to learn from small amounts of data. It’s called cooperative meta-learning and helps machines generalize better. The idea is to have two types of learners: one that updates its parameters in both inner and outer loops, and another that only updates its parameters in the outer loop. This second learner helps find better starting points for the first learner without using up extra resources or slowing it down. The approach works well with different types of data and tasks, such as recognizing images and classifying nodes. By injecting noise into the model’s gradient, the framework improves its ability to generalize. |
Keywords
» Artificial intelligence » Classification » Few shot » Generalization » Image classification » Meta learning » Regression » Regularization
