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Neural network task specialization via domain constraining

arXiv 2504.19592 ↗First posted 28 Apr 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 36 references that are in the map. First posted on arXiv 28 Apr 2025.

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References · 22

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10 more
  1. 2018A mathematical theory of semantic development in deep neural networkscited by 182
  2. 2018Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learningcited by 174
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  6. 2025Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Modelscited by 53
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  10. 2025Hypernym Bias: Unraveling Deep Classifier Training Dynamics through the Lens of Class Hierarchycited by 2

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