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

• URL: http://arxiv.org/abs/2112.13121v1
• Date: Fri, 24 Dec 2021 18:42:58 GMT
• Title: The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence
• Authors: Brando Miranda, Yu-Xiong Wang and Sanmi Koyejo
• Abstract summary: A transfer learning solution might be all we needed to solve many few-shot learning benchmarks. We name this metric the diversity coefficient of a few-shot learning benchmark. We show that when making a fair comparison between MAML learned solutions to transfer learning, both have identical meta-test accuracy.
• Score: 19.556093984142418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It has been recently observed that a transfer learning solution might be all we needed to solve many few-shot learning benchmarks. This raises important questions about when and how meta-learning algorithms should be deployed. In this paper, we make a first step in clarifying these questions by first formulating a computable metric for a few-shot learning benchmark that we hypothesize is predictive of whether meta-learning solutions will succeed or not. We name this metric the diversity coefficient of a few-shot learning benchmark. Using the diversity coefficient, we show that the MiniImagenet benchmark has zero diversity - according to twenty-four different ways to compute the diversity. We proceed to show that when making a fair comparison between MAML learned solutions to transfer learning, both have identical meta-test accuracy. This suggests that transfer learning fails to outperform MAML - contrary to what previous work suggests. Together, these two facts provide the first test of whether diversity correlates with meta-learning success and therefore show that a diversity coefficient of zero correlates with a high similarity between transfer learning and MAML learned solutions - especially at meta-test time. We therefore conjecture meta-learned solutions have the same meta-test performance as transfer learning when the diversity coefficient is zero.

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