There is a growing interest in learning data representations that work well
for many different types of problems and data. In this paper, we look in
particular at the task of learning a single visual representation that can be
successfully utilized in the analysis of very different types of images, from
dog breeds to stop signs and digits. Inspired by recent work on learning
networks that predict the parameters of another, we develop a tunable deep
network architecture that, by means of adapter residual modules, can be steered
on the fly to diverse visual domains. Our method achieves a high degree of
parameter sharing while maintaining or even improving the accuracy of
domain-specific representations. We also introduce the Visual Decathlon
Challenge, a benchmark that evaluates the ability of representations to capture
simultaneously ten very different visual domains and measures their ability to
recognize well uniformly.

该论文介绍了一种利用适配器残模块进行数据表示学习的深度网络架构，该网络能够被灵活地指引至不同的视觉领域中，实现了高度的参数共享并保持或提高了特定领域表示的准确性。同时，该论文还提出了 Visual Decathlon Challenge 基准评测系统，用于同时评估表征的能力并测量其识别不同领域的能力。