Fetching the paper…
Reading the bibliography…
Few-shot classification is a challenging problem due to the uncertainty caused by using few labelled samples.
Y. Wang, W. Chao, K. Q. Weinberger, L. van der Maaten, Simpleshot: Revisiting nearest-neighbor classification for few-shot learning , CoRR abs/1911.04623 · 1911
Earlier work this paper cites.
R. DIAgostino, An omnibus test of normality for moderate and large sample sizes, Biometrika 58 (34) (1971) 1–348
1971
Earlier work this paper cites.
R. D’AGOSTINO, E. S. Pearson, Tests for departure from normality. empirical results for the distributions of b 2 and b \sqrt{b} , Biometrika 60 (3) (1973) 613–622
1973
Earlier work this paper cites.
J. W. Tukey, Exploratory data analysis, Vol. 2, Reading, Mass., 1977
1977
Earlier work this paper cites.
A. P. Dempster, N. M. Laird, D. B. Rubin, Maximum likelihood from incomplete data via the em algorithm, Journal of the Royal Statistical Society: Series B (Methodological) 39 (1) (1977) 1–22
1977
Earlier work this paper cites.
S. M. Kye, H. Lee, H. Kim, S. J. Hwang, Transductive few-shot learning with meta-learned confidence , CoRR abs/2002.12017 · 2002
Earlier work this paper cites.
C. Villani, Optimal transport: old and new, Vol. 338, Springer Science & Business Media, 2008
2008
Earlier work this paper cites.
M. Boudiaf, I. M. Ziko, J. Rony, J. Dolz, P. Piantanida, I. B. Ayed, Transductive information maximization for few-shot learning , CoRR abs/2008.11297 · 2008
Earlier work this paper cites.
O. Chapelle, B. Scholkopf, A. Zien, Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews], IEEE Transactions on Neural Networks 20 (3) (2009) 542–542
2009
Earlier work this paper cites.
L. Torrey, J. Shavlik, Transfer learning, in: Handbook of research on machine learning applications and trends: algorithms, methods, and techniques, IGI Global, 2010, pp. 242–264
2010
Earlier work this paper cites.
C. Wah, S. Branson, P. Welinder, P. Perona, S. Belongie, The Caltech-UCSD Birds-200-2011 Dataset, Tech. Rep. CNS-TR-2011-001, California Institute of Technology (2011)
2011
Earlier work this paper cites.
S. Thrun, L. Pratt, Learning to learn, Springer Science & Business Media, 2012
2012
Earlier work this paper cites.
T. Mensink, J. Verbeek, F. Perronnin, G. Csurka, Metric learning for large scale image classification: Generalizing to new classes at near-zero cost, in: European Conference on Computer Vision, Springer, 2012, pp. 488–501
2012
Earlier work this paper cites.
M. Cuturi, Sinkhorn distances: Lightspeed computation of optimal transport, in: Advances in neural information processing systems, 2013, pp. 2292–2300
2013
Earlier work this paper cites.
R. G. Cinbis, J. Verbeek, C. Schmid, Approximate fisher kernels of non-iid image models for image categorization, IEEE transactions on pattern analysis and machine intelligence 38 (6) (2015) 1084–1098
2015
Earlier work this paper cites.
J. Solomon, F. De Goes, G. Peyré, M. Cuturi, A. Butscher, A. Nguyen, T. Du, L. Guibas, Convolutional wasserstein distances: Efficient optimal transportation on geometric domains, ACM Transactions on Graphics (TOG) 34 (4) (2015) 1–11
2015
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al., Imagenet large scale visual recognition challenge, International journal of computer vision 115 (3) (2015) 211–252
2015
Earlier work this paper cites.
S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: International conference on machine learning, PMLR, 2015, pp. 448–456
2015
Cited alongside, same era.
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra, et al., Matching networks for one shot learning, in: Advances in neural information processing systems, 2016, pp. 3630–3638
2016
Cited alongside, same era.
S. Zagoruyko, N. Komodakis, Wide residual networks , in: R. C. Wilson, E. R. Hancock, W. A. P. Smith (Eds.), Proceedings of the British Machine Vision Conference 2016, BMVC 2016, York, UK, September 19-22, 2016, BMVA Press, 2016. URL http://www.bmva.org/bmvc/2016/papers/paper087/index.html
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, 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
Cited alongside, same era.
Z. Chen, Y. Fu, Y.-X. Wang, L. Ma, W. Liu, M. Hebert, Image deformation meta-networks for one-shot learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 8680–8689
2019
Later among the works it cites.
Y. Liu, J. Lee, M. Park, S. Kim, E. Yang, S. J. Hwang, Y. Yang, Learning to propagate labels: Transductive propagation network for few-shot learning , in: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019, OpenReview.net, 2019. URL https://openreview.net/forum?id=SyVuRiC5K7
2019
Later among the works it cites.
A. A. Rusu, D. Rao, J. Sygnowski, O. Vinyals, R. Pascanu, S. Osindero, R. Hadsell, Meta-learning with latent embedding optimization , in: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019, OpenReview.net, 2019. URL https://openreview.net/forum?id=BJgklhAcK7
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Bendale, T. E. Boult, Towards open set deep networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 1563–1572
2016
Cited alongside, same era.
C. Finn, P. Abbeel, S. Levine, Model-agnostic meta-learning for fast adaptation of deep networks, in: Proceedings of the 34th International Conference on Machine Learning-Volume 70, JMLR. org, 2017, pp. 1126–1135
2017
Cited alongside, same era.
S. Ravi, H. Larochelle, Optimization as a model for few-shot learning , in: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, OpenReview.net, 2017. URL https://openreview.net/forum?id=rJY0-Kcll
2017
Cited alongside, same era.
G. Huang, Z. Liu, L. Van Der Maaten, K. Q. Weinberger, Densely connected convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4700–4708
2017
Cited alongside, same era.
J. Snell, K. Swersky, R. Zemel, Prototypical networks for few-shot learning, in: Advances in Neural Information Processing Systems, 2017, pp. 4077–4087
2017
Cited alongside, same era.
P. R. M. Júnior, R. M. De Souza, R. d. O. Werneck, B. V. Stein, D. V. Pazinato, W. R. de Almeida, O. A. Penatti, R. d. S. Torres, A. Rocha, Nearest neighbors distance ratio open-set classifier, Machine Learning 106 (3) (2017) 359–386
2017
Cited alongside, same era.
V. Gripon, G. B. Hacene, M. Löwe, F. Vermet, Improving accuracy of nonparametric transfer learning via vector segmentation, in: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp. 2966–2970
2018
Cited alongside, same era.
M. Ren, E. Triantafillou, S. Ravi, J. Snell, K. Swersky, J. B. Tenenbaum, H. Larochelle, R. S. Zemel, Meta-learning for semi-supervised few-shot classification , in: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings, OpenReview.net, 2018. URL https://openreview.net/forum?id=HJcSzz-CZ
2018
Cited alongside, same era.
V. Verma, A. Lamb, C. Beckham, A. Najafi, I. Mitliagkas, D. Lopez-Paz, Y. Bengio, Manifold mixup: Better representations by interpolating hidden states, in: International Conference on Machine Learning, PMLR, 2019, pp. 6438–6447
2019
Later among the works it cites.
P. Mangla, N. Kumari, A. Sinha, M. Singh, B. Krishnamurthy, V. N. Balasubramanian, Charting the right manifold: Manifold mixup for few-shot learning, in: The IEEE Winter Conference on Applications of Computer Vision, 2020, pp. 2218–2227
2020
Later among the works it cites.
M. Lichtenstein, P. Sattigeri, R. Feris, R. Giryes, L. Karlinsky, Tafssl: Task-adaptive feature sub-space learning for few-shot classification, in: European Conference on Computer Vision, Springer, 2020, pp. 522–539
2020
Later among the works it cites.
C. Zhang, Y. Cai, G. Lin, C. Shen, Deepemd: Few-shot image classification with differentiable earth mover’s distance and structured classifiers, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 12203–12213
2020
Later among the works it cites.
H.-J. Ye, H. Hu, D.-C. Zhan, F. Sha, Few-shot learning via embedding adaptation with set-to-set functions, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 8808–8817
2020
Later among the works it cites.
J. Liu, L. Song, Y. Qin, Prototype rectification for few-shot learning, in: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16, Springer, 2020, pp. 741–756
2020
Later among the works it cites.
I. Ziko, J. Dolz, E. Granger, I. B. Ayed, Laplacian regularized few-shot learning, in: International Conference on Machine Learning, PMLR, 2020, pp. 11660–11670
2020
Later among the works it cites.
P. Rodríguez, I. Laradji, A. Drouin, A. Lacoste, Embedding propagation: Smoother manifold for few-shot classification, in: European Conference on Computer Vision, Springer, 2020, pp. 121–138
2020
Later among the works it cites.
C. Simon, P. Koniusz, R. Nock, M. Harandi, Adaptive subspaces for few-shot learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 4136–4145
2020
Later among the works it cites.
B. Liu, H. Kang, H. Li, G. Hua, N. Vasconcelos, Few-shot open-set recognition using meta-learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 8798–8807
2020
Later among the works it cites.
Y. Hu, V. Gripon, S. Pateux, Graph-based interpolation of feature vectors for accurate few-shot classification, in: 2020 25th International Conference on Pattern Recognition (ICPR), IEEE, 2021, pp. 8164–8171
2021
Closest in time.
Y. Hu, V. Gripon, S. Pateux, Leveraging the feature distribution in transfer-based few-shot learning, in: International Conference on Artificial Neural Networks, Springer, 2021, pp. 487–499
2021
Closest in time.
S. Yang, L. Liu, M. Xu, Free lunch for few-shot learning: Distribution calibration , in: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021, OpenReview.net, 2021. URL https://openreview.net/forum?id=JWOiYxMG92s
2021
Closest in time.