Continual Deep Hash Learning for Dynamic Image Databases with Margin-Adaptive Semantic Preservation - Dylan Hrawford; Brnav Ahuja (Bioinformatics Insights and Analytics, 2026)
The proliferation of large-scale dynamic image databases in visually driven domains such as autonomous surveillance, medical imaging archives, and social media platforms demands retrieval systems that not only respond to queries with sublinear latency but also continuously adapt to evolving data distributions without incurring exorbitant retraining costs. Deep hashing has emerged as a powerful paradigm that encodes high-dimensional visual features into compact binary codes, enabling efficient approximate nearest neighbor search in Hamming space. However, conventional deep hashing models assume static data corpora and require full re-training upon database growth or semantic drift, a practice that is computationally wasteful, environmentally unsustainable, and incompatible with real-time service-level agreements. This paper proposes a framework for continual deep hash learning that integrates a margin-adaptive semantic preservation mechanism derived from self-supervised asymmetric semantic excavation. Approached from a systems perspective, the work analyzes the architectural trade-offs involved in constructing incrementally updatable hash models, the governance of semantic boundaries in nonstationary feature spaces, and the infrastructure required for sustainable deployment at Internet scale. The discussion encompasses robustness under distributional shift, fairness across diverse user communities, privacy compliance in lifelong learning pipelines, and the policy implications of autonomous model evolution. By grounding the technical design in socio-technical infrastructure thinking, this paper provides a comprehensive roadmap for building retrieval systems that are simultaneously accurate, adaptable, responsible, and operationally viable.