Fetching the paper…
Reading the bibliography…
Few-Shot classification aims at solving problems that only a few samples are available in the training process.
Y. Rubner, C. Tomasi, and L. J. Guibas, “A metric for distributions with applications to image databases,” in Sixth International Conference on Computer Vision (IEEE Cat. No. 98CH36271) . IEEE, 1998, pp. 59–66
1998
Earlier work this paper cites.
C. E. Rasmussen, “Gaussian processes in machine learning,” in Summer school on machine learning . Springer, 2003, pp. 63–71
2003
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on Knowledge and Data Engineering , vol. 22, no. 10, pp. 1345–1359, 2010
2010
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Convolutional deep belief networks on cifar-10,” Unpublished manuscript , vol. 40, no. 7, pp. 1–9, 2010
2010
Earlier work this paper cites.
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona, “Caltech-ucsd birds 200,” 2010
2010
Earlier work this paper cites.
J. Krause, M. Stark, J. Deng, and L. Fei-Fei, “3d object representations for fine-grained categorization,” in Proceedings of the IEEE international conference on computer vision workshops , 2013, pp. 554–561
2013
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv e-prints , pp. arXiv–1412, 2014
2014
Earlier work this paper cites.
M. Jiang, P. Cui, X. Chen, F. Wang, W. Zhu, and S. Yang, “Social recommendation with cross-domain transferable knowledge,” IEEE Transactions on Knowledge and Data Engineering , vol. 27, no. 11, pp. 3084–3097, 2015
2015
Earlier work this paper cites.
G. Peyré, M. Cuturi, and J. Solomon, “Gromov-wasserstein averaging of kernel and distance matrices,” in International Conference on Machine Learning . PMLR, 2016, pp. 2664–2672
2016
Earlier work this paper cites.
S. Ravi and H. Larochelle, “Optimization as a model for few-shot learning,” Proceedings ofthe 5th International Conference on Learning Representations (ICLR) , 2016
2016
Earlier work this paper cites.
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra et al. , “Matching networks for one shot learning,” Advances in neural information processing systems , vol. 29, pp. 3630–3638, 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Graves, M. G. Bellemare, J. Menick, R. Munos, and K. Kavukcuoglu, “Automated curriculum learning for neural networks,” in international conference on machine learning . PMLR, 2017, pp. 1311–1320
2017
Earlier work this paper cites.
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba, “Places: A 10 million image database for scene recognition,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 6, pp. 1452–1464, 2017
2017
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in International Conference on Machine Learning . PMLR, 2017, pp. 1126–1135
2017
Cited alongside, same era.
D. Weinshall, G. Cohen, and D. Amir, “Curriculum learning by transfer learning: Theory and experiments with deep networks,” in Proceedings of the 35th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, J. Dy and A. Krause, Eds., vol. 80. PMLR, 10–15 Jul 2018, pp. 5238–5246. [Online]. Available: https://proceedings.mlr.press/v80/weinshall18a.html
2018
Cited alongside, same era.
B. Muzellec and M. Cuturi, “Generalizing point embeddings using the wasserstein space of elliptical distributions,” in Proceedings of the 32nd International Conference on Neural Information Processing Systems , 2018, pp. 10 258–10 269
2018
Cited alongside, same era.
C. Frogner, F. Mirzazadeh, and J. Solomon, “Learning embeddings into entropic wasserstein spaces,” in International Conference on Learning Representations , 2018
L. Chen, Z. Gan, Y. Cheng, L. Li, L. Carin, and J. Liu, “Graph optimal transport for cross-domain alignment,” in International Conference on Machine Learning . PMLR, 2020, pp. 1542–1553
2020
Later among the works it cites.
2020
Later among the works it cites.
W. Wang, G. Xu, W. Ding, Y. Huang, G. Li, J. Tang, and Z. Liu, “Representation learning from limited educational data with crowdsourced labels,” IEEE Transactions on Knowledge and Data Engineering , pp. 1–1, 2020
2020
Later among the works it cites.
L. Jing and Y. Tian, “Self-supervised visual feature learning with deep neural networks: A survey,” IEEE transactions on pattern analysis and machine intelligence , 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of Big Data , vol. 6, no. 1, pp. 1–48, 2019
2019
Cited alongside, same era.
A. Achille, M. Lam, R. Tewari, A. Ravichandran, S. Maji, C. C. Fowlkes, S. Soatto, and P. Perona, “Task2vec: Task embedding for meta-learning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 6430–6439
2019
Cited alongside, same era.
G. Peyré, M. Cuturi et al. , “Computational optimal transport: With applications to data science,” Foundations and Trends® in Machine Learning , vol. 11, no. 5-6, pp. 355–607, 2019
2019
Cited alongside, same era.
V. Titouan, N. Courty, R. Tavenard, and R. Flamary, “Optimal transport for structured data with application on graphs,” in International Conference on Machine Learning . PMLR, 2019, pp. 6275–6284
2019
Cited alongside, same era.
X. Guo, X. Liu, E. Zhu, X. Zhu, M. Li, X. Xu, and J. Yin, “Adaptive self-paced deep clustering with data augmentation,” IEEE Transactions on Knowledge and Data Engineering , vol. 32, no. 9, pp. 1680–1693, 2019
2019
Cited alongside, same era.
Y. Tang, Y. Xie, X. Yang, J. Niu, and W. Zhang, “Tensor multi-elastic kernel self-paced learning for time series clustering,” IEEE Transactions on Knowledge and Data Engineering , 2019
2019
Cited alongside, same era.
G. Hacohen and D. Weinshall, “On the power of curriculum learning in training deep networks,” in Proceedings of the 36th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, K. Chaudhuri and R. Salakhutdinov, Eds., vol. 97. PMLR, 09–15 Jun 2019, pp. 2535–2544. [Online]. Available: https://proceedings.mlr.press/v97/hacohen19a.html
2019
Cited alongside, same era.
T. Matiisen, A. Oliver, T. Cohen, and J. Schulman, “Teacher–student curriculum learning,” IEEE transactions on neural networks and learning systems , vol. 31, no. 9, pp. 3732–3740, 2019
2019
Cited alongside, same era.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9729–9738
2020
Later among the works it cites.
2020
Later among the works it cites.
Y. Guo, N. C. Codella, L. Karlinsky, J. V. Codella, J. R. Smith, K. Saenko, T. Rosing, and R. Feris, “A broader study of cross-domain few-shot learning,” in European Conference on Computer Vision . Springer, 2020, pp. 124–141
2020
Later among the works it cites.
T. Adler, J. Brandstetter, M. Widrich, A. Mayr, D. Kreil, M. Kopp, G. Klambauer, and S. Hochreiter, “Cross-domain few-shot learning by representation fusion,” arXiv e-prints , pp. arXiv–2010, 2020
2020
Later among the works it cites.
H.-Y. Tseng, H.-Y. Lee, J.-B. Huang, and M.-H. Yang, “Cross-domain few-shot classification via learned feature-wise transformation,” in International Conference on Learning Representations , 2020
2020
Later among the works it cites.
2021
Later among the works it cites.
B. Wallace, Z. Wu, and B. Hariharan, “Can we characterize tasks without labels or features?” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 1245–1254
2021
Later among the works it cites.
W. Huang, J. Liu, T. Li, T. Huang, S. Ji, and J. Wan, “Feddsr: Daily schedule recommendation in a federated deep reinforcement learning framework,” IEEE Transactions on Knowledge and Data Engineering , pp. 1–1, 2021
2021
Later among the works it cites.
B. Han, I. W. Tsang, X. Xiao, L. Chen, S.-F. Fung, and C. P. Yu, “Privacy-preserving stochastic gradual learning,” IEEE Transactions on Knowledge and Data Engineering , vol. 33, no. 8, pp. 3129–3140, 2021
2021
Later among the works it cites.
X. Wang, Y. Chen, and W. Zhu, “A survey on curriculum learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence , pp. 1–1, 2021
2021
Later among the works it cites.
Q. Zhang, W. Liao, G. Zhang, B. Yuan, and J. Lu, “A deep dual adversarial network for cross-domain recommendation,” IEEE Transactions on Knowledge and Data Engineering , pp. 1–1, 2021
2021
Later among the works it cites.