The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence
This work addresses the failure of meta-learning to surpass transfer learning in low-diversity benchmarks, providing a test for diversity's role in meta-learning success, though it is incremental in clarifying existing empirical observations.
The paper tackles the problem of when meta-learning outperforms transfer learning in few-shot learning by introducing a diversity coefficient metric, finding that MiniImagenet has zero diversity, which correlates with equivalent performance between transfer learning and MAML.
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.