# The Omniglot challenge: a 3-year progress report

## TL;DR

This paper reviews three years of progress on the Omniglot dataset, highlighting advancements in one-shot classification and the need for further research on more complex tasks to achieve human-like concept learning.

## Contribution

It provides a comprehensive progress report on the Omniglot challenge, emphasizing the dataset's adoption, progress made, and remaining challenges in developing models that learn across multiple tasks.

## Key findings

- Significant progress in one-shot classification accuracy.
- Researchers have adopted easier task splits and procedures.
- Less progress has been made on the other four challenge tasks.

## Abstract

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.