  Meta-learning methods have been extensively studied and applied in computer
vision, especially for few-shot classification tasks. The key idea of
meta-learning for few-shot classification is to mimic the few-shot situations
faced at test time by randomly sampling classes in meta-training data to
construct few-shot tasks for episodic training. While a rich line of work
focuses solely on how to extract meta-knowledge across tasks, we exploit the
complementary problem on how to generate informative tasks. We argue that the
randomly sampled tasks could be sub-optimal and uninformative (e.g., the task
of classifying "dog" from "laptop" is often trivial) to the meta-learner. In
this paper, we propose an adaptive task sampling method to improve the
generalization performance. Unlike instance based sampling, task based sampling
is much more challenging due to the implicit definition of the task in each
episode. Therefore, we accordingly propose a greedy class-pair based sampling
method, which selects difficult tasks according to class-pair potentials. We
evaluate our adaptive task sampling method on two few-shot classification
benchmarks, and it achieves consistent improvements across different feature
backbones, meta-learning algorithms and datasets.
