Neural network task specialization via domain constraining

2025/04/28 by R. O. Malashin, Malashin, Roman, Daniil Ilyukhin +1
Computer Science · #Advanced Neural Network Applications #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Face recognition and analysis

paper · pdf · doi:10.48550/arxiv.2504.19592

openalex publication_date 2025/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

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.

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Related

• Effective Domain Knowledge Transfer with Soft Fine-tuning
• DATA: Domain-Aware and Task-Aware Self-supervised Learning
• Learning Good Features to Transfer Across Tasks and Domains
• Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution
• Episodic Training for Domain Generalization