Revisiting Meta-Learning as Supervised Learning

02/03/2020
by   Wei-Lun Chao, et al.
0

Recent years have witnessed an abundance of new publications and approaches on meta-learning. This community-wide enthusiasm has sparked great insights but has also created a plethora of seemingly different frameworks, which can be hard to compare and evaluate. In this paper, we aim to provide a principled, unifying framework by revisiting and strengthening the connection between meta-learning and traditional supervised learning. By treating pairs of task-specific data sets and target models as (feature, label) samples, we can reduce many meta-learning algorithms to instances of supervised learning. This view not only unifies meta-learning into an intuitive and practical framework but also allows us to transfer insights from supervised learning directly to improve meta-learning. For example, we obtain a better understanding of generalization properties, and we can readily transfer well-understood techniques, such as model ensemble, pre-training, joint training, data augmentation, and even nearest neighbor based methods. We provide an intuitive analogy of these methods in the context of meta-learning and show that they give rise to significant improvements in model performance on few-shot learning.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/09/2020

A New Meta-Baseline for Few-Shot Learning

Meta-learning has become a popular framework for few-shot learning in re...
research
09/18/2019

Meta-Neighborhoods

Traditional methods for training neural networks use training data just ...
research
11/24/2019

Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning

Exploiting known semantic relationships between fine-grained tasks is cr...
research
06/21/2019

Meta-learning of textual representations

Recent progress in AutoML has lead to state-of-the-art methods (e.g., Au...
research
04/12/2023

Meta-Learned Models of Cognition

Meta-learning is a framework for learning learning algorithms through re...
research
06/09/2022

Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning

Brain networks characterize complex connectivities among brain regions a...
research
06/08/2021

Meta-Learning to Compositionally Generalize

Natural language is compositional; the meaning of a sentence is a functi...

Please sign up or login with your details

Forgot password? Click here to reset