Artificial intelligence

Transfer Learning: Image Recognition with Pre-Trained Brains

By: Vajratiya Vajrobol, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, vvajratiya@gmail.com

Consider the vast quantity of data required to train a model to accurately identify every thing seen in a picture, However, imagine if we could utilize pre-existing information, such as fundamental shapes and colors, to expedite the learning process. Transfer learning emerges as a guiding light in the expansion of picture recognition.

The Essence of Transfer Learning:

Think of transfer learning as taking a shortcut in image recognition. Instead of building a model from scratch, we utilize the expertise of pre-trained models like VGG16 or ResNet. These models have already devoured millions of images from colossal datasets like ImageNet, learning the fundamental building blocks of visual understanding – edges, textures, shapes, and more [1,2].

The process of using transfer learning in image recognition

  1. Pre-Trained Foundation: leveraging the pre-trained model as a sturdy foundation, its lower layers packed with general visual knowledge. It’s like having a seasoned architect’s blueprint for building with basic shapes and structures[3].
  2. Feature Extraction: extracting valuable features from new images by passing them through the pre-trained model’s lower layers. Imagine identifying circles for eyes and triangles for ears while looking at a dog picture.
  3. New Task-Specific Layers: adding specialized layers on top of the extracted features, tailored to our specific task [4].
  4. Fine-Tuning (Optional): further refining the pre-trained model by adjusting some of its lower layers. It’s like changing the foundation slightly to better support the specific features.

The Benefits of Transfer Learning

  • Faster Learning: Pre-trained models give our new model a start, significantly reducing training time and data requirements. No need to show millions of cat pictures to distinguish them from dogs [5].
  • Better Accuracy: inheriting the expertise of a model trained on vast datasets, often surpassing models trained from scratch, especially with limited data. Think of the difference between relying on basic shapes versus having an architect guide the design [6].
  • Reduced Resources: saving precious time, computational power, and data thanks to the existing knowledge base. Building on a strong foundation eliminates the need for heavy-duty construction from scratch [7].

Transfer learning empowers a variety of image recognition tasks

  • Object Detection: From autonomous cars identifying pedestrians to security systems spotting intruders, transfer learning helps models differentiate objects in real-time, making our world safer [8].
  • Medical Imaging: Analyzing medical scans for diseases like cancer requires high accuracy. Transfer learning helps build models that can detect subtle abnormalities, leading to earlier diagnoses and better treatment outcomes [9].
  • Satellite Imagery: Understanding our planet’s changes needs efficient image analysis. Transfer learning helps classify land cover, track deforestation, and even monitor crop health, contributing to sustainable practices [10].
  • Art Generation: Style transfer, image synthesis, and creative applications all benefit from the pre-trained knowledge base, opening doors to artistic exploration [11].

With continuous research and advancements in pre-trained models, transfer learning holds immense potential for the future of image recognition. We can expect even more impressive applications in healthcare, environmental monitoring, and beyond.

References

  1. Mukti, I. Z., & Biswas, D. (2019, December). Transfer learning based plant diseases detection using ResNet50. In 2019 4th International conference on electrical information and communication technology (EICT) (pp. 1-6). IEEE.
  2. Saber, A., Sakr, M., Abo-Seida, O. M., Keshk, A., & Chen, H. (2021). A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique. IEEE Access, 9, 71194-71209.
  3. Marcelino, P. (2018). Transfer learning from pre-trained models. Towards data science, 10, 23.
  4. Lu, T., & Dooms, A. (2019, September). A deep transfer learning approach to document image quality assessment. In 2019 International Conference on Document Analysis and Recognition (ICDAR) (pp. 1372-1377). IEEE.
  5. Raghu, M., Zhang, C., Kleinberg, J., & Bengio, S. (2019). Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems, 32.
  6. Hussain, M., Bird, J. J., & Faria, D. R. (2019). A study on cnn transfer learning for image classification. In Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK (pp. 191-202). Springer International Publishing.
  7. Salehi, A. W., Khan, S., Gupta, G., Alabduallah, B. I., Almjally, A., Alsolai, H., … & Mellit, A. (2023). A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope. Sustainability, 15(7), 5930.
  8. Yabuki, N., Nishimura, N., & Fukuda, T. (2018). Automatic object detection from digital images by deep learning with transfer learning. In Advanced Computing Strategies for Engineering: 25th EG-ICE International Workshop 2018, Lausanne, Switzerland, June 10-13, 2018, Proceedings, Part I 25 (pp. 3-15). Springer International Publishing.
  9. Alzubaidi, L., Fadhel, M. A., Al-Shamma, O., Zhang, J., Santamaría, J., Duan, Y., & R. Oleiwi, S. (2020). Towards a better understanding of transfer learning for medical imaging: a case study. Applied Sciences, 10(13), 4523.
  10. Chen, Z., Zhang, T., & Ouyang, C. (2018). End-to-end airplane detection using transfer learning in remote sensing images. Remote Sensing, 10(1), 139.
  11. Achicanoy, H., Chaves, D., & Trujillo, M. (2021). StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications. Symmetry, 13(8), 1497.
  12. Poonia, V., Goyal, M. K., Gupta, B. B., Gupta, A. K., Jha, S., & Das, J. (2021). Drought occurrence in different river basins of India and blockchain technology based framework for disaster management. Journal of Cleaner Production, 312, 127737.
  13. Gupta, B. B., & Sheng, Q. Z. (Eds.). (2019). Machine learning for computer and cyber security: principle, algorithms, and practices. CRC Press.
  14. Singh, A., & Gupta, B. B. (2022). Distributed denial-of-service (DDoS) attacks and defense mechanisms in various web-enabled computing platforms: issues, challenges, and future research directions. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-43.
  15. Almomani, A., Alauthman, M., Shatnawi, M. T., Alweshah, M., Alrosan, A., Alomoush, W., & Gupta, B. B. (2022). Phishing website detection with semantic features based on machine learning classifiers: a comparative study. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-24.

Cite As:

Vajrobol V. (2024) Transfer Learning: Image Recognition with Pre-Trained Brains, Insights2Techinfo, pp.1

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