This paper introduces a concept of neural network specialization via task-specific domain constraining, aimed at enhancing network performance on data subspace in which the network operates. The study presents experiments on training specialists for image classification and object detection tasks. The results demonstrate that specialization can enhance a generalist's accuracy even without additional data or changing training regimes: solely by constraining class label space in which the network performs. Theoretical and experimental analyses indicate that effective specialization requires modifying traditional fine-tuning methods and constraining data space to semantically coherent subsets. The specialist extraction phase before tuning the network is proposed for maximal performance gains. We also provide analysis of the evolution of the feature space during specialization. This study paves way to future research for developing more advanced dynamically configurable image analysis systems, where computations depend on the specific input. Additionally, the proposed methods can help improve system performance in scenarios where certain data domains should be excluded from consideration of the generalist network.

本研究解决了神经网络在特定任务上性能不足的问题，提出了一种通过领域约束实现任务专门化的新方法。实验证明，该方法可以在不增加数据或改变训练方式的情况下，提高网络的准确度，尤其是在图像分类和对象检测任务中。这项研究为开发更高级的动态可配置图像分析系统开辟了新的方向。 

通过领域约束实现神经网络任务专门化