MetaDL: Few Shot Learning Competition with Novel Datasets from Practical Domains + Q&A

Meta-learning is an important machine learning paradigm leveraging experience from previous tasks to make better predictions on the task at hand. This competition focuses on supervised learning, and more particularly `few shot learning' classification settings, aiming at learning a good model from very few examples, typically 1 to 5 per class. A starting kit will be provided, consisting of a public dataset and various baseline implementations, including MAML (Finn et al., 2017) and Prototypical Networks (Snell et al., 2017). This way, it should be easy to get started and build upon the various resources in the field. The competition consists of novel datasets from various domains, including healthcare, ecology, biology, and chemistry. The competition will consist of three phases: a public phase, a feedback phase, and a final phase. The last two phases will be run with code submissions, fully bind-tested on the Codalab challenge platform. A single (final) submission will be evaluated during the final phase, using five fresh datasets, currently unknown to the meta-learning community.