# Learning to Generalize to Unseen Tasks with Bilevel Optimization

## TL;DR

This paper introduces a bilevel optimization framework called learning to generalize (L2G) for metric-based meta-learning, explicitly improving the model's ability to generalize to unseen classes in few-shot classification tasks.

## Contribution

It proposes a novel bilevel optimization approach that explicitly constrains meta-learning to enhance generalization to unseen classes during training.

## Key findings

- L2G significantly improves performance over episodic training on mini-ImageNet and tiered-ImageNet.
- L2G produces a metric space that better clusters and separates unseen classes.
- The framework is validated with Prototypical and Relation Networks.

## Abstract

Recent metric-based meta-learning approaches, which learn a metric space that generalizes well over combinatorial number of different classification tasks sampled from a task distribution, have been shown to be effective for few-shot classification tasks of unseen classes. They are often trained with episodic training where they iteratively train a common metric space that reduces distance between the class representatives and instances belonging to each class, over large number of episodes with random classes. However, this training is limited in that while the main target is the generalization to the classification of unseen classes during training, there is no explicit consideration of generalization during meta-training phase. To tackle this issue, we propose a simple yet effective meta-learning framework for metricbased approaches, which we refer to as learning to generalize (L2G), that explicitly constrains the learning on a sampled classification task to reduce the classification error on a randomly sampled unseen classification task with a bilevel optimization scheme. This explicit learning aimed toward generalization allows the model to obtain a metric that separates well between unseen classes. We validate our L2G framework on mini-ImageNet and tiered-ImageNet datasets with two base meta-learning few-shot classification models, Prototypical Networks and Relation Networks. The results show that L2G significantly improves the performance of the two methods over episodic training. Further visualization shows that L2G obtains a metric space that clusters and separates unseen classes well.