Timezone: »

A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches
Vincent Dumoulin · Neil Houlsby · Utku Evci · Xiaohua Zhai · Ross Goroshin · Sylvain Gelly · Hugo Larochelle

Meta and transfer learning are two successful families of approaches to few-shot learning. Despite highly related goals, state-of-the-art advances in each family are measured largely in isolation of each other. As a result of diverging evaluation norms, a direct or thorough comparison of different approaches is challenging. To bridge this gap, we introduce a few-shot classification evaluation protocol named VTAB+MD with the explicit goal of facilitating sharing of insights from each community. We demonstrate its accessibility in practice by performing a cross-family study of the best transfer and meta learners which report on both a large-scale meta-learning benchmark (Meta-Dataset, MD), and a transfer learning benchmark (Visual Task Adaptation Benchmark, VTAB). We find that, on average, large-scale transfer methods (Big Transfer, BiT) outperform competing approaches on MD, even when trained only on ImageNet. In contrast, meta-learning approaches struggle to compete on VTAB when trained and validated on MD. However, BiT is not without limitations, and pushing for scale does not improve performance on highly out-of-distribution MD tasks. We hope that this work contributes to accelerating progress on few-shot learning research.

Author Information

Vincent Dumoulin (Google Research)
Neil Houlsby (Google)
Utku Evci (Google Montreal)
Xiaohua Zhai (Google Brain)
Ross Goroshin (Google Brain)
Sylvain Gelly (Google Brain)
Hugo Larochelle (Google Brain)

More from the Same Authors