|
A. Rusu, A., D. Rao, J. Sygnowski, O. Vinyals, R. Pascanu, S. Osindero, and R.
Hadsell, “Meta-learning with latent embedding optimization,” arXiv:1807.05960,
2018.
[2] S. Ravi and H. Larochelle, “Optimization as a model for few-shot learning,” ICLR,
2016.
[3] B. Hariharan and R. Girshick, “Low-shot visual recognition by shrinking and
hallucinating features,” CV, pp. 3018-3027, 2017.
[4] O. Vinyals, C. Blundell, T. Lillicrap, and D. Wierstra, “Matching networks for one
shot learning,” NIPS, 29, 3630-3638, 2016.
[5] A. Banerjee, S. Merugu, S. Dhillon I., J. Ghosh, and J. Lafferty, “Clustering with
Bregman divergences,” JMLR, Vol. 6, No. 10, 2005.
[6] C. Zhang, Y. Cai, G. Lin, and C. Shen, “DeepEMD: Differentiable Earth Mover's
Distance for Few-Shot Learning,” arXiv: 2003. 06777, 2020.
[7] C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast
adaptation of deep networks,” ICML, pp. 1126-1135, 2017.
[8] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural
computation, 9(8), 1735-1780, 1997.
[9] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale
image recognition,” arXiv: 1409.1556, 2014.
[10] A. Krizhevsky, I. Sutskever, and E. Hinton G, “Imagenet classification with deep
convolutional neural networks,” NIPS, 25, 1097-1105, 2012.
[11] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, and A. Rabinovich,
“Going deeper with convolutions,” CVPR, pp. 1-9, 2015.
[12] S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training
42
by reducing internal covariate shift,” ICML, pp. 448-456, PMLR, 2015.
[13] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition.”
CVPR, pp. 770-778, 2016.
[14] J. Snell, K. Swersky, and S. Zemel R., “Prototypical networks for few-shot
learning,” arXiv: 1703, 05175, 2017.
[15] Y. Rubner, C. Tomasi, and L. Guibas J., “The earth mover's distance as a metric for
image retrieval,” CV, Vol. 40, No. 2, 99-121, 2000.
[16] D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly
learning to align and translate,” arXiv: 1409. 0473, 2014.
[17] K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, and Y. Bengio,
“Show, attend and tell: Neural image caption generation with visual attention,”
ICML, pp. 2048-2057, PMLR, 2015.
[18] B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, “Learning deep
features for discriminative localization,” CVPR, pp. 2921-2929, 2016.
[19] R. Selvaraju, R., M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra.
“Grad-cam: Visual explanations from deep networks via gradient-based
localization” CV, pp. 618-626, 2017.
[20] X. Li, X. Yang, Z. Ma, and H. Xue J., “Deep Metric Learning for Few-Shot Image
Classification: A Selective Review,” arXiv: 2105. 08149, 2021.
[21] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, and L. Fei-Fei,
“Imagenet large scale visual recognition challenge,” CV, Vol. 115, No. 3, 211-252,
2015.
|