Availability of labelled data is the major obstacle to the deployment of deep learning algorithms for computer vision tasks in new domains. The fact that many frameworks adopted to solve different tasks share the same architecture suggests that there should be a way of reusing the knowledge learned in a specific setting to solve novel tasks with limited or no additional supervision. In this work, we first show that such knowledge can be shared across tasks by learning a mapping between task-specific deep features in a given domain. Then, we show that this mapping function, implemented by a neural network, is able to generalize to novel unseen domains. Besides, we propose a set of strategies to constrain the learned feature spaces, to ease learning and increase the generalization capability of the mapping network, thereby considerably improving the final performance of our framework. Our proposal obtains compelling results in challenging synthetic-to-real adaptation scenarios by transferring knowledge between monocular depth estimation and semantic segmentation tasks.

翻译：标签化数据的可用性是深度学习算法在新领域计算机视觉任务中部署的主要障碍。许多用于解决不同任务的框架采用相同架构这一事实表明，应存在一种方法，可将特定设置下学到的知识复用于解决新任务，且仅需有限或无需额外监督。在本工作中，我们首先展示通过学习给定域内任务特定深度特征之间的映射，此类知识可跨任务共享。随后，我们证明由神经网络实现的该映射函数能够泛化至未见过的全新领域。此外，我们提出一系列策略以约束学得的特征空间，从而简化学习过程并增强映射网络的泛化能力，进而显著提升我们框架的最终性能。通过将知识在单目深度估计与语义分割任务之间迁移，我们的方法在具有挑战性的合成到真实场景适应中获得了令人信服的结果。