Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark
This work addresses the lack of direct comparison between transfer and meta learning for few-shot learning, facilitating cross-community insights and progress.
The study compared transfer and meta learning approaches on unified few-shot classification benchmarks, finding that large-scale transfer methods like BiT outperformed meta-learning on Meta-Dataset, while meta-learning struggled on VTAB, with BiT showing limitations on out-of-distribution tasks.
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 perform a cross-family study of the best transfer and meta learners 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. In performing this study, we reveal a number of discrepancies in evaluation norms and study some of these in light of the performance gap. We hope that this work facilitates sharing of insights from each community, and accelerates progress on few-shot learning.