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There is a growing interest in learning data representations that work wellfor many different types of problems and data. In this paper, we look inparticular at the task of learning a single visual representation that can besuccessfully utilized in the analysis of very different types of images, fromdog breeds to stop signs and digits. Inspired by recent work on learningnetworks that predict the parameters of another, we develop a tunable deepnetwork architecture that, by means of adapter residual modules, can be steeredon the fly to diverse visual domains. Our method achieves a high degree ofparameter sharing while maintaining or even improving the accuracy ofdomain-specific representations. We also introduce the Visual DecathlonChallenge, a benchmark that evaluates the ability of representations to capturesimultaneously ten very different visual domains and measures their ability torecognize well uniformly.

Learning multiple visual domains with residual adapters