# Fast Adaptation with Kernel and Gradient based Meta Leaning

arXiv: https://arxiv.org/abs/2411.00404 · HTML: https://mlsanity.com/paper/2411.00404/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [Domain Generalization via Invariant Feature Representation](https://mlsanity.com/paper/1301.2115/) (2013)
- [TADAM: Task dependent adaptive metric for improved few-shot learning](https://mlsanity.com/paper/1805.10123/) (2018)
- [Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML](https://mlsanity.com/paper/1909.09157/) (2019)
- [How to train your MAML](https://mlsanity.com/paper/1810.09502/) (2018)
- [Torchmeta: A Meta-Learning library for PyTorch](https://mlsanity.com/paper/1909.06576/) (2019)

## Abstract (arXiv)

Model Agnostic Meta Learning or MAML has become the standard for few-shot learning as a meta-learning problem. MAML is simple and can be applied to any model, as its name suggests. However, it often suffers from instability and computational inefficiency during both training and inference times. In this paper, we propose two algorithms to improve both the inner and outer loops of MAML, then pose an important question about what 'meta' learning truly is. Our first algorithm redefines the optimization problem in the function space to update the model using closed-form solutions instead of optimizing parameters through multiple gradient steps in the inner loop. In the outer loop, the second algorithm adjusts the learning of the meta-learner by assigning weights to the losses from each task of the inner loop. This method optimizes convergence during both the training and inference stages of MAML. In conclusion, our algorithms offer a new perspective on meta-learning and make significant discoveries in both theory and experiments. This research suggests a more efficient approach to few-shot learning and fast task adaptation compared to existing methods. Furthermore, it lays the foundation for establishing a new paradigm in meta-learning.