멀티모달 딥러닝 기술과 수자원 분야 활용

  • 발행 : 2023.06.30

초록

키워드

참고문헌

  1. 조윤상, 이지윤, 이영재, 강현규, 김성범.(2019). Multimodal Deep Learning for Explainable Product Design Data. 대한산업공학회 추계학술대회 논문집, 1184-1216.
  2. Andrew, G., Arora, R., Bilmes, J., & Livescu, K. (2013). Deep canonical correlation analysis. In ICML.
  3. Wang, W., Arora, R., Livescu, K., & Bilmes, J. A. (2015). Unsupervised learning of acoustic features via deep canonical correlation analysis. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4590-4594). South Brisbane, QLD, Australia: IEEE. doi:10.1109/ICASSP.2015.7178840.
  4. Akkus, C., Chu, L., Djakovic, V., Jauch-Walser, S., Koch, P., Loss, G., & Assenmacher, M. (2023). Multimodal Deep Learning. arXiv preprint arXiv:2301.04856.
  5. Morency, L.P., & Howes, C. (2017). Multimodal machine learning tutorial. Retrieved from https://www.cs.cmu.edu/~morency/MMML-Tutorial-ACL2017.pdf
  6. Ma, L., Lu, Z., Shang, L., & Li, H. (2015). Multimodal convolutional neural networks for matching image and sentence. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2623-2631).
  7. Mao, J., Xu, W., Yang, Y., Wang, J., & Yuille, A. L. (2014). Explain images with multimodal recurrent neural networks. arXiv preprint arXiv:1410.1090.
  8. Yoon, S., Kim, S., & Bae, S. (2023). Application of multimodal deep learning using radar and water level data for water level prediction. In EGU General Assembly 2023, Vienna, Austria, 24-28 Apr 2023 (EGU23-5818).