Learning multiple visual domains with residual adapters

Understand

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.