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We propose unsupervised embedding adaptation for the downstream few-shot classification task.
Learning from one example through shared densities on transforms
Miller, E. G., Matsakis, N. E., and Viola, P. A · 2000
Earlier work this paper cites.
Caltech-ucsd birds 200
Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., and Perona, P · 2010
Earlier work this paper cites.
Learning to learn
Thrun, S. and Pratt, L · 2012
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Earlier work this paper cites.
Estimating local intrinsic dimensionality
Amsaleg, L., Chelly, O., Furon, T., Girard, S., Houle, M. E., Kawarabayashi, K., and Nett, M · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Earlier work this paper cites.
Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al · 2016
Earlier work this paper cites.
Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M. and Favaro, P · 2016
Earlier work this paper cites.
Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al · 2016
Earlier work this paper cites.
The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality
Amsaleg, L., Bailey, J., Barbe, D., Erfani, S. M., Houle, M. E., Nguyen, V., and Radovanovic, M · 2017
Earlier work this paper cites.
A closer look at memorization in deep networks
Arpit, D., Jastrzebski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A. C., Bengio, Y., and Lacoste-Julien, S · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Earlier work this paper cites.
Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R. S · 2017
Earlier work this paper cites.
Unsupervised representation learning by predicting image rotations
Gidaris, S., Singh, P., and Komodakis, N · 2018
Earlier work this paper cites.
Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Jiang, L., Zhou, Z., Leung, T., Li, L.-J., and Fei-Fei, L · 2018
Earlier work this paper cites.
Tadam: Task dependent adaptive metric for improved few-shot learning
Oreshkin, B. N., Rodriguez, P., and Lacoste, A · 2018
Earlier work this paper cites.
Meta-learning for semi-supervised few-shot classification
Ren, M., Ravi, S., Triantafillou, E., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., and Zemel, R. S · 2018
Earlier work this paper cites.
Co-teaching: Robust training of deep neural networks with extremely noisy labels
Sugiyama, M · 2018
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Intrinsic dimension of data representations in deep neural networks
Ansuini, A., Laio, A., Macke, J. H., and Zoccolan, D · 2019
Cited alongside, same era.
Learning to learn by self-critique
Antoniou, A. and Storkey, A. J · 2019
Cited alongside, same era.
A closer look at few-shot classification
Chen, W., Liu, Y., Kira, Z., Wang, Y. F., and Huang, J · 2019
Cited alongside, same era.
Generating classification weights with GNN denoising autoencoders for few-shot learning
Gidaris, S. and Komodakis, N · 2019
Cited alongside, same era.
Boosting few-shot visual learning with self-supervision
Gidaris, S., Bursuc, A., Komodakis, N., Perez, P., and Cord, M · 2019
Cited alongside, same era.
S4l: Self-supervised semi-supervised learning
Zhai, X., Oliver, A., Kolesnikov, A., and Beyer, L · 2019
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Information maximization for few-shot learning
Boudiaf, M., Ziko, I. M., Rony, J., Dolz, J., Piantanida, P., and Ayed, I. B · 2020
Later among the works it cites.
Unsupervised learning of visual features by contrasting cluster assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., and Joulin, A · 2020
Later among the works it cites.
Big self-supervised models are strong semi-supervised learners
Chen, T., Kornblith, S., Swersky, K., Norouzi, M., and Hinton, G · 2020
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A baseline for few-shot image classification
Dhillon, G. S., Chaudhari, P., Ravichandran, A., and Soatto, S · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., et al · 2020
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On the intrinsic dimensionality of image representations
Gong, S., Boddeti, V. N., and Jain, A. K · 2019
Cited alongside, same era.
Using pre-training can improve model robustness and uncertainty
Hendrycks, D., Lee, K., and Mazeika, M · 2019
Cited alongside, same era.
Cross attention network for few-shot classification
Hou, R., Chang, H., Ma, B., Shan, S., and Chen, X · 2019
Cited alongside, same era.
Edge-labeling graph neural network for few-shot learning
Kim, J., Kim, T., Kim, S., and Yoo, C. D · 2019
Cited alongside, same era.
An analytic theory of generalization dynamics and transfer learning in deep linear networks
Lampinen, A. K. and Ganguli, S · 2019
Cited alongside, same era.
Finding task-relevant features for few-shot learning by category traversal
Li, H., Eigen, D., Dodge, S., Zeiler, M., and Wang, X · 2019
Cited alongside, same era.
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Empirical bayes transductive meta-learning with synthetic gradients
Hu, S. X., Moreno, P. G., Xiao, Y., Shen, X., Obozinski, G., Lawrence, N. D., and Damianou, A. C · 2020
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TAFSSL: task-adaptive feature sub-space learning for few-shot classification
Lichtenstein, M., Sattigeri, P., Feris, R., Giryes, R., and Karlinsky, L · 2020
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Prototype rectification for few-shot learning
Liu, J., Song, L., and Qin, Y · 2020
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Embedding propagation: Smoother manifold for few-shot classification
Rodríguez, P., Laradji, I., Drouin, A., and Lacoste, A · 2020
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How does early stopping help generalization against label noise?
Song, H., Kim, M., Park, D., and Lee, J.-G · 2020
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When does self-supervision improve few-shot learning?
Su, J.-C., Maji, S., and Hariharan, B · 2020
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Rethinking few-shot image classification: a good embedding is all you need?
Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J. B., and Isola, P · 2020
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Few-shot learning via embedding adaptation with set-to-set functions
Ye, H., Hu, H., Zhan, D., and Sha, F · 2020
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Laplacian regularized few-shot learning
Ziko, I. M., Dolz, J., Granger, E., and Ayed, I. B · 2020
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Selfmatch: Combining contrastive self-supervision and consistency for semi-supervised learning
Kim, B., Choo, J., Kwon, Y.-D., Joe, S., Min, S., and Gwon, Y · 2021
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On the geometry of generalization and memorization in deep neural networks
Stephenson, C., suchismita padhy, Ganesh, A., Hui, Y., Tang, H., and Chung, S · 2021
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