Deep learning has achieved state-of-the-art performance on several computer vision tasks and domains. Nevertheless, it still has a high computational cost and demands a significant amount of parameters. Such requirements hinder the use in resource-limited environments and demand both software and hardware optimization. Another limitation is that deep models are usually specialized into a single domain or task, requiring them to learn and store new parameters for each new one. Multi-Domain Learning (MDL) attempts to solve this problem by learning a single model that is capable of performing well in multiple domains. Nevertheless, the models are usually larger than the baseline for a single domain. This work tackles both of these problems: our objective is to prune models capable of handling multiple domains according to a user defined budget, making them more computationally affordable while keeping a similar classification performance. We achieve this by encouraging all domains to use a similar subset of filters from the baseline model, up to the amount defined by the user's budget. Then, filters that are not used by any domain are pruned from the network. The proposed approach innovates by better adapting to resource-limited devices while, to our knowledge, being the only work that is capable of handling multiple domains at test time with fewer parameters and lower computational complexity than the baseline model for a single domain.

翻译：深度学习在多个计算机视觉任务和领域中已取得最先进的性能，但其计算成本高昂且参数量巨大，这些限制阻碍了在资源受限环境中的应用，需要软硬件协同优化。另一个局限在于深度模型通常专用于单一领域或任务，每次面对新领域或任务时都需要学习并存储新参数。多领域学习（MDL）试图通过训练一个能同时在多个领域取得优异性能的单一模型来解决该问题，但此类模型通常比单领域基线模型更庞大。本研究同时解决这两个问题：我们的目标是基于用户定义的预算约束，对能处理多领域的模型进行剪枝，在保持分类性能的同时提升计算可行性。具体而言，我们通过鼓励所有领域共享基线模型中相似子集滤波器（数量不超过用户预算上限）来实现这一目标，随后从网络中移除所有领域均未使用的滤波器。该方法创新性地适配了资源受限设备，据我们所知，这是唯一能在测试阶段以少于单领域基线模型的参数数量和计算复杂度处理多领域的工作。