The Effect of Diversity in Meta-Learning

TL;DR

This paper investigates the impact of task diversity in meta-learning, revealing that increased diversity does not always enhance performance and that similar manifolds can be learned with less diverse data, challenging conventional assumptions.

Contribution

The study provides empirical and theoretical evidence that higher task diversity can sometimes hinder meta-learning performance, questioning existing beliefs about data diversity benefits.

Findings

Lower task diversity can learn similar manifolds as higher diversity

Increasing task diversity does not always improve meta-learning performance

Empirical and theoretical analysis supports these conclusions

Abstract

Recent studies show that task distribution plays a vital role in the meta-learner's performance. Conventional wisdom is that task diversity should improve the performance of meta-learning. In this work, we find evidence to the contrary; (i) our experiments draw into question the efficacy of our learned models: similar manifolds can be learned with a subset of the data (lower task diversity). This finding questions the advantage of providing more data to the model, and (ii) adding diversity to the task distribution (higher task diversity) sometimes hinders the model and does not lead to a significant improvement in performance as previously believed. To strengthen our findings, we provide both empirical and theoretical evidence.