1 |
J. Snell, K. Swersky, and R.S. Zemel, "Proto-Typical Networks for Few-Shot Learning," arXiv Preprint, arXiv:1703.05175, 2017.
|
2 |
F. Sung, Y. Yang, L. Zhang, T. Xiang, P.H. Torr, and T.M. Hospedales, "Learning to Compare: Relation Network for Few-Shot Learning," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199-1208, 2018.
|
3 |
O. Vinyals, C. Blundell, T. Lillicrap, and D. Wierstra, "Matching Networks for One Shot Learning," Advances in Neural Information Processing Systems, pp. 3630-3638, 2016.
|
4 |
C. Finn, P. Abbeel, and S. Levine, "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks," International Conference on Machine Learning, Vol. 70, pp. 1126-1135, 2017.
|
5 |
S. Ravi and H. Larochelle, "Optimization as a Model for Few-Shot Learning," International Conference on Learning Representations, 2017.
|
6 |
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, The Caltech-UCSD Birds200-2011 Dataset, Technical Report, 2011.
|
7 |
W.Y. Chen, Y.C. Liu, Z. Kira, Y.C.F. Wang, and J.B. Huang, "A Closer Look at Few-Shot Classification," arXiv P reprint, arXiv:1904.04232, 2019.
|
8 |
K. Mahajan, M. Sharma, and L. Vig, "Meta-DermDiagnosis: Few-Shot Skin Disease Identification Using Meta-Learning," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 730-731. 2020.
|
9 |
M. Ren, E. Triantafillou, S. Ravi, J. Snell, K. Swersky, J.B. Tenenbaum, et al., "Meta-Learning for Semi-Supervised Few-Shot Classification," arXiv P reprint, arXiv:1803.00676, 2018.
|
10 |
K. Lee, S. Maji, A. Ravichandran, and S. Soatto, "Meta-Learning with Differentiable Convex Optimization," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10657-10665, 2019.
|
11 |
A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap, "Meta-Learning with Memory-Augmented Neural Networks," International Conference on Machine Learning, pp. 1842-1850, 2016.
|
12 |
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, "End-to-End Object Detection with Transformers," European Conference on Computer Vision, pp. 213-229, 2020.
|
13 |
F. Jiang, A. Grigorev, S. Rho, Z. Tian, Y. Fu, W. Jifara, et al., "Medical Image Semantic Segmentation Based on Deep Learning," Neural Computing and Applications, Vol. 29, No. 5, pp. 1257-1265, 2018.
DOI
|
14 |
A. Nichol, and J. Schulman, "Reptile: A Scalable Metalearning Algorithm," arXiv P reprint, arXiv:1803.02999, 2018.
|
15 |
X. Yang, Y. Ye, X. Li, R.Y. Lau, X. Zhang, and X. Huang, "Hyperspectral Image Classification with Deep Learning Models," IEEE Transactions on Geoscience and Remote Sensing, Vol. 56, No. 9, pp. 5408-5423, 2018.
DOI
|
16 |
N. Zhang, Y. Feng, and E.J. Lee, "Activity Object Detection Based on Improved Faster R-CNN," Journal of Korea Multimedia Society, Vol. 24, No. 3, pp. 416-422, 2021.
DOI
|
17 |
S.W. Park and D.Y. Kim, "Comparison of Image Classification Performance in Convolutional Neural Network According to Transfer Learning," Journal of Korea Multimedia Society, Vol. 21 No. 12, pp. 1387-1395, 2018.
DOI
|
18 |
X.L. Huang, C.Z. Kim, and S.H. Choi, "An Automatic Strabismus Screening Method with Corneal Light Reflex Based on Image Processing," Journal of Korea Multimedia Society, Vol. 24, No. 5, pp. 642-650, 2021.
DOI
|
19 |
T. Munkhdalai and H. Yu, "Meta Networks," International Conference on Machine Learning, pp. 2554-2563. 2017.
|