  Three years ago, we released the Omniglot dataset for one-shot learning,
along with five challenge tasks and a computational model that addresses these
tasks. The model was not meant to be the final word on Omniglot; we hoped that
the community would build on our work and develop new approaches. In the time
since, we have been pleased to see wide adoption of the dataset. There has been
notable progress on one-shot classification, but researchers have adopted new
splits and procedures that make the task easier. There has been less progress
on the other four tasks. We conclude that recent approaches are still far from
human-like concept learning on Omniglot, a challenge that requires performing
many tasks with a single model.
