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

TL;DR

This paper introduces the diversity coefficient to measure task diversity in few-shot learning benchmarks, revealing that low diversity correlates with transfer learning and MAML performing similarly, thus explaining when meta-learning offers advantages.

Contribution

The paper proposes a novel diversity coefficient metric to predict the effectiveness of meta-learning versus transfer learning based on task diversity in benchmarks.

Findings

MiniImagenet has zero diversity according to the metric

Transfer learning and MAML perform similarly when diversity is zero

Diversity coefficient predicts meta-learning success

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 proposing a novel metric -- the diversity coefficient -- to measure the diversity of tasks in a few-shot learning benchmark. We hypothesize that the diversity coefficient of the few-shot learning benchmark is predictive of whether meta-learning solutions will succeed or not. Using the diversity coefficient, we show that the MiniImagenet benchmark has zero diversity. This novel insight contextualizes claims that transfer learning solutions are better than meta-learned solutions. Specifically, we empirically find that a diversity coefficient of zero correlates with a high similarity between transfer learning and Model-Agnostic Meta-Learning (MAML) learned solutions in terms of meta-accuracy (at meta-test time). Therefore, we conjecture meta-learned solutions have the same meta-test performance as transfer learning when the diversity coefficient is zero. Our work provides the first test of whether diversity correlates with meta-learning success.