The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence

Brando Miranda, Patrick Yu, Yu-Xiong Wang, Oluwasanmi O Koyejo

Keywords: meta-learning, few-shot learning, machine learning, deep learning

TL;DR: when the task diversity of few-shot learning benchmarks is low and comparison is fair, MAML and transfer learning perform the same -- opposite of claims that transfer learning is better

Abstract:

Recently, it has been observed that a transfer learning solution might be all we need to solve many few-shot learning benchmarks -- thus raising important questions about when and how meta-learning algorithms should be deployed.
In this paper, we seek to clarify these questions by
1. proposing a novel metric -- the {\it diversity coefficient} -- to measure the diversity of tasks in a few-shot learning benchmark and
2. by comparing MAML and transfer learning under fair conditions (same architecture, same optimizer and all models trained to convergence).
Using the diversity coefficient, we show that the popular MiniImagenet and Cifar-fs few-shot learning benchmarks have low diversity.
This novel insight contextualizes claims that transfer learning solutions are better than meta-learned solutions in the regime of low diversity under a fair comparison.
Specifically, we empirically find that a low diversity coefficient correlates with a high similarity between transfer learning and Model-Agnostic Meta-Learning (MAML) learned solutions in terms of accuracy at meta-test time and classification layer similarity (using feature based distance metrics like SVCCA, PWCCA, CKA, and OPD).
To further support our claim, we find this meta-test accuracy holds even as the model size changes.
Therefore, we conclude that in the low diversity regime, MAML and transfer learning have equivalent meta-test performance when both are compared fairly.
We also hope our work inspires more thoughtful constructions and quantitative evaluations of meta-learning benchmarks in the future.