Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators

A simple but effective way for few-shot classification in which a task distribution spans multiple domains including ones never seen during meta-training, to build a pool of models to cover this wide task distribution and learn to select the best one for a particular task through cross-domain meta-learning.