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 handles 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上达到了最先进的性能。然而，其计算成本高昂且需要大量参数，这限制了在资源受限环境中的使用，并需要软硬件协同优化。另一个局限性在于，深度模型通常专精于单一领域或任务，若需扩展至新领域，则必须学习并存储新参数。多领域学习（MDL）旨在通过训练一个能同时在多个领域取得良好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大。本研究同时针对这两个问题：目标是依据用户定义的预算剪枝能处理多领域的模型，在保持相似分类性能的同时降低计算成本。我们通过鼓励所有领域使用基线模型中相似子集过滤器（数量不超过用户预算上限）来实现目标，随后从网络中删除未被任何领域使用的过滤器。所提方法能更好地适应资源受限设备，据我们所知，这是唯一能在测试时用更少参数和更低计算复杂度（相较于单领域基线模型）处理多领域任务的研究。