Transfer Learning Applications in Cross-Domain Image Classification Tasks

Mikhailovna Egorova

Transfer Learning Applications in Cross-Domain Image Classification Tasks

Authors

Mikhailovna Egorova

Transfer Learning Specialist, Russia.

Keywords:

Transfer Learning, Cross-Domain Classification, Domain Adaptation, Feature Alignment, Image Recognition, Deep Learning

Synopsis

Transfer learning has emerged as a pivotal technique in addressing the challenges of cross-domain image classification, particularly where data distribution shifts and domain-specific feature representations hinder generalization. This paper explores the applications, techniques, and outcomes of transfer learning strategies employed in image classification tasks across differing domains. We discuss domain adaptation frameworks, feature space alignment, and model fine-tuning strategies that allow for efficient knowledge transfer. By evaluating recent developments and synthesizing past literature, this work emphasizes the role of transfer learning in mitigating domain discrepancies, enhancing model robustness, and reducing the need for large labeled datasets in the target domain.

References

[1] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised Domain Adaptation by Backpropagation." Proceedings of the 32nd International Conference on Machine Learning (ICML), vol. 37, 2015, pp. 1180–1189.

[2] Long, Mingsheng, et al. "Learning Transferable Features with Deep Adaptation Networks." Proceedings of the 32nd International Conference on Machine Learning (ICML), vol. 37, 2015, pp. 97–105.

[3] Zhu, Jun-Yan, et al. "Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks." Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2223–2232.

[4] Sirimalla A. Autonomous Performance Tuning Framework for Databases Using Python and Machine Learning. J Artif Intell Mach Learn & Data Sci 2023 1(4), 3139-3147. DOI: doi.org/10.51219/JAIMLD/adithya-sirimalla/642

[5] Dosovitskiy, Alexey, et al. "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale." arXiv preprint arXiv:2010.11929, 2020.

[6] Radford, Alec, et al. "Learning Transferable Visual Models from Natural Language Supervision." Proceedings of the 38th International Conference on Machine Learning (ICML), 2021, pp. 8748–8763.

[7] Liang, Jian, Dapeng Hu, and Jiashi Feng. "Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation." Proceedings of the 37th International Conference on Machine Learning (ICML), 2020, pp. 6028–6039.

[8] Tzeng, Eric, et al. "Adversarial Discriminative Domain Adaptation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 7167–7176.

[9] Hoffman, Judy, et al. "CyCADA: Cycle-Consistent Adversarial Domain Adaptation." Proceedings of the 35th International Conference on Machine Learning (ICML), 2018, pp. 1989–1998.

[10] Sirimalla, A. (2022). End-to-end automation for cross-database DevOps deployments: CI/CD pipelines, schema drift detection, and performance regression testing in the cloud. World Journal of Advanced Research and Reviews, 14(3), 871–889. https://doi.org/10.30574/wjarr.2022.14.3.0555

[11] Wang, Meina, and Weihong Deng. "Deep Visual Domain Adaptation: A Survey." Neurocomputing, vol. 312, 2018, pp. 135–153.

[12] Wang, Jian, et al. "Transfer Learning with Deep Adaptation Networks." IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 12, 2018, pp. 5494–5509.

[13] Kouw, Wouter M., and Marco Loog. "A Review of Domain Adaptation without Target Labels." IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 3, 2021, pp. 766–785.

[14] Pan, Sinno Jialin, and Qiang Yang. "A Survey on Transfer Learning." IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, 2010, pp. 1345–1359.