In this work, we present a new, algorithm for multi-domain learning. Given a pretrained architecture and a set of visual domains received sequentially, the goal of multi-domain learning is to produce a single model performing a task in all the domains together. Recent works showed how we can address this problem by masking the internal weights of a given original convnet through learned binary variables. In this work, we provide a general formulation of binary mask-based models for multi-domain learning by affine transformations of the original network parameters. Our formulation obtains significantly higher levels of adaptation to new domains, achieving performances comparable to domain-specific models while requiring slightly more than 1 bit per network parameter per additional domain. Experiments on two popular benchmarks showcase the power of our approach, achieving performances close to state-of-the-art methods on the Visual Decathlon Challenge.

中文翻译：

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通过仿射变换提升二进制掩码以进行多域学习

在这项工作中，我们提出了一种用于多域学习的新算法。给定一个经过预先训练的体系结构并依次接收一组可视域，多域学习的目标是产生一个在所有域中共同执行任务的单一模型。最近的工作显示了如何通过学习的二进制变量掩盖给定原始卷积网络的内部权重来解决此问题。在这项工作中，我们通过对原始网络参数进行仿射变换，为多域学习提供了基于二进制掩码的模型的一般表述。我们的公式获得了对新域的明显更高的适应性，可实现与特定于域的模型相当的性能，而每个附加域的每个网络参数要求略多于1位。