Learning multiple visual domains with residual adapters

NeurIPS 2017 Sylvestre-Alvise RebuffiHakan BilenAndrea Vedaldi

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... (read more)

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