Hard-Meta-Dataset++: Towards Understanding Few-Shot Performance on Difficult Tasks - Basu, Samyadeep; Stanley, Megan; Bronskill, John; Feizi, Soheil; Massiceti, Daniela (2023 International Conference on Learning Representations, 2023)
Few-shot classification is the ability to adapt to any new classification task from only a few training examples. The performance of current top-performing few-shot classifiers varies widely across different tasks where they often fail on a sub-set of ‘difficult’ tasks. This phenomenon has real-world consequences for deployed few-shot systems where safety and reliability are paramount, yet little has been done to understand these failure cases. In this paper, we study these difficult tasks to gain a more nuanced understanding of the limitations of current methods. To this end, we develop a general and computationally efficient algorithm called FastDiffSel to extract difficult tasks from any large-scale vision dataset. Notably, our algorithm can extract tasks at least 20x faster than existing methods enabling its use on large-scale datasets. We use FastDiffSel to extract difficult tasks from Meta-Dataset, a widely-used few-shot classification benchmark, and other challenging large-scale vision datasets including ORBIT, CURE-OR and ObjectNet. These tasks are curated into Hard-Meta-Dataset++, a new few-shot classification testing benchmark to promote the development of methods that are robust to even the most difficult tasks. We use Hard-Meta-Dataset++ to stress-test an extensive suite of few-shot classification methods and show that state-of-the-art approaches fail catastrophically on difficult tasks. We believe that our extraction algorithm FastDiffSel and Hard-Meta-Dataset++ will aid researchers in further understanding failure modes of few-shot classification models.