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.

研究人员指出，近年来在 Omniglot 数据集上的一个确切的挑战是以单一的模型完成多项任务的人类概念学习，虽然研究人员在一项分类任务上取得了显著进展，但其他四项任务仍有待解决。

Omniglot 挑战：三年进展报告

The Omniglot challenge: a 3-year progress report

Multitask learning, i.e. learning several tasks at once with the same neural
network, can improve performance in each of the tasks. Designing deep neural
network architectures for multitask learning is a challenge: There are many
ways to tie the tasks together, and the design choices matter. The size and
complexity of this problem exceeds human design ability, making it a compelling
domain for evolutionary optimization. Using the existing state of the art soft
ordering architecture as the starting point, methods for evolving the modules
of this architecture and for evolving the overall topology or routing between
modules are evaluated in this paper. A synergetic approach of evolving custom
routings with evolved, shared modules for each task is found to be very
powerful, significantly improving the state of the art in the Omniglot
multitask, multialphabet character recognition domain. This result demonstrates
how evolution can be instrumental in advancing deep neural network and complex
system design in general.

通过进化优化，结合自定义路由和共享模块，提高了 Omniglot 的多任务、多字符识别的表现，为深度神经网络和复杂系统设计提供了重要思路。