TRANSFER LEARNING'S TRIUMPH IN COMPUTER VISION: A BOON FOR EFFICIENCY AND PERFORMANCE
Keywords:
Efficiency, Transfer Learning, Computer Vision, Deep Learning, Feature Extraction, Efficiency, PerformanceAbstract
This article explores the transformative impact of transfer learning in computer vision, addressing the challenges of extensive data requirements and computational demands in traditional approaches. It discusses the concept and methodology of transfer learning, including model selection, fine-tuning techniques, and layer-freezing strategies. The article highlights the significant advantages of transfer learning, such as reduced data requirements, accelerated training times, and improved model performance. It presents real-world applications across object detection, image classification, and other computer vision tasks. Finally, the article examines future directions in transfer learning research, including advanced techniques and cross-domain transfer, emphasizing the potential for further advancements in the field.
References
E. Strubell, A. Ganesh, and A. McCallum, "Energy and Policy Considerations for Deep Learning in NLP," in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019, pp. 3645-3650. [Online]. Available: https://aclanthology.org/P19-1355/
J. Deng, "ImageNet: A large-scale hierarchical image database," in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248-255. [Online]. Available: https://ieeexplore.ieee.org/document/5206848
A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," in Advances in Neural
Information Processing Systems 25, 2012, pp. 1097-1105. [Online]. Available: https://papers.nips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779-788. [Online]. Available: https://ieeexplore.ieee.org/document/7780460
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, "How transferable are features in deep neural networks?," in Advances in Neural Information Processing Systems 27, 2014, pp. 3320-3328. [Online]. Available: https://papers.nips.cc/paper/2014/file/375c71349b295fbe2dcdca9206f20a06-Paper.pdf
K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778. [Online]. Available: https://ieeexplore.ieee.org/document/7780459
S. Kornblith, J. Shlens, and Q. V. Le, "Do Better ImageNet Models Transfer Better?," in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 2661-2671. [Online]. Available: https://ieeexplore.ieee.org/document/8953714
J. Howard and S. Ruder, "Universal Language Model Fine-tuning for Text Classification," in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018, pp. 328-339. [Online]. Available: https://aclanthology.org/P18-1031/
S. Kornblith, J. Shlens, and Q. V. Le, "Do Better ImageNet Models Transfer Better?," in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 2661-2671. [Online]. Available: https://ieeexplore.ieee.org/document/8953714
M. Raghu, C. Zhang, J. Kleinberg, and S. Bengio, "Transfusion: Understanding Transfer Learning for Medical Imaging," in Advances in Neural Information Processing Systems 32, 2019, pp. 3347-3357. [Online]. Available: https://papers.nips.cc/paper/2019/file/eb1e78328c46506b46a4ac4a1e378b91-Paper.pdf
J. Redmon and A. Farhadi, "YOLOv3: An Incremental Improvement," arXiv preprint arXiv:1804.02767, 2018. [Online]. Available: https://arxiv.org/abs/1804.02767
A. Esteva, "Dermatologist-level classification of skin cancer with deep neural networks," Nature, vol. 542, no. 7639, pp. 115-118, 2017. [Online]. Available: https://www.nature.com/articles/nature21056
C. Finn, P. Abbeel, and S. Levine, "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks," in Proceedings of the 34th International Conference on Machine Learning, 2017, pp. 1126-1135. [Online]. Available: http://proceedings.mlr.press/v70/finn17a.html
A. R. Zamir, "Taskonomy: Disentangling Task Transfer Learning," in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 3712-3722. [Online]. Available: https://ieeexplore.ieee.org/document/8578523
