Continual Deep Hash Learning for Dynamic Image Databases with Margin-Adaptive Semantic Preservation
Keywords:
continual learning, deep hashing, image retrieval, margin adaptation, system architecture, fairness, governance, sustainabilityAbstract
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.
References
1. Wang, J., Liu, W., Kumar, S., & Chang, S. F. (2016). Learning to hash for indexing big data—A survey. Proceedings of the IEEE, 104(1), 34–57.
2. Cao, Y., Long, M., Wang, J., & Liu, B. (2017). HashNet: Deep learning to hash by continuation. In Proceedings of the IEEE International Conference on Computer Vision (pp. 5609–5618).
3. Li, Q., Sun, Z., He, R., & Tan, T. (2017). Deep supervised discrete hashing. In Advances in Neural Information Processing Systems (pp. 2594–2603).
4. Zhu, H., Long, M., Wang, J., & Cao, Y. (2016). Deep hashing network for efficient similarity retrieval. In Proceedings of the AAAI Conference on Artificial Intelligence.
5. Liu, H., Wang, R., Shan, S., & Chen, X. (2016). Deep supervised hashing for fast image retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
6. Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019). Continual lifelong learning with neural networks: A review. Neural Networks, 113, 54–71.
7. Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., … & Hadsell, R. (2017). Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences, 114(13), 3521–3526.
8. Li, Z., & Hoiem, D. (2017). Learning without forgetting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12), 2935–2947.
9. Lopez-Paz, D., & Ranzato, M. (2017). Gradient episodic memory for continual learning. In Advances in Neural Information Processing Systems (pp. 6467–6476).
10. Rebuffi, S. A., Kolesnikov, A., Sperl, G., & Lampert, C. H. (2017). iCaRL: Incremental classifier and representation learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2001–2010).
11. Cakir, F., & Sclaroff, S. (2015). Adaptive hashing for fast similarity search. In IEEE International Conference on Computer Vision (pp. 1044–1052).
12. Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 815–823).
13. Cao, Y., Long, M., Wang, J., & Liu, B. (2018). Deep Cauchy hashing for Hamming space retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1229–1237).
14. Li, Q., Sun, Z., He, R., & Tan, T. (2017). Deep pairwise-supervised hashing. In Proceedings of the International Joint Conference on Artificial Intelligence (pp. 1971–1977).
15. Jiang, Q., & Li, W. (2018). Asymmetric deep supervised hashing. In Proceedings of the AAAI Conference on Artificial Intelligence (pp. 3342–3349).
16. Yu, Z., Wu, S., Dou, Z., & Bakker, E. M. (2022). Deep hashing with self-supervised asymmetric semantic excavation and margin-scalable constraint. Neurocomputing, 483, 87-104.
17. Shen, Y., Qin, J., Huang, L., Liu, L., Zhu, F., & Shao, L. (2020). Adversarial attack on deep hashing with pairwise label manipulation. In Proceedings of the AAAI Conference on Artificial Intelligence (pp. 11931–11938).
18. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35.
19. Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning: Limitations and Opportunities. fairmlbook.org.
20. Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76–99.
21. Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59–68).
22. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3645–3650).
23. Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L. M., Rothchild, D., … & Dean, J. (2021). Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350.
24. Shokri, R., & Shmatikov, V. (2015). Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (pp. 1310–1321).
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