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We investigate the role of self-supervised learning (SSL) in the context of few-shot learning.
Nilsback, M.E., Zisserman, A.: A visual vocabulary for flower classification. In: CVPR (2006)
2006
Earlier work this paper cites.
Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., Perona, P.: Caltech-UCSD Birds 200. Tech. Rep. CNS-TR-2010-001, California Institute of Technology (2010)
2010
Earlier work this paper cites.
Khosla, A., Jayadevaprakash, N., Yao, B., Fei-Fei, L.: Novel dataset for fine-grained image categorization. In: First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011)
2011
Earlier work this paper cites.
Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3D object representations for fine-grained categorization. In: 4th International IEEE Workshop on 3D Representation and Recognition (3DRR). Sydney, Australia (2013)
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
Dosovitskiy, A., Springenberg, J.T., Riedmiller, M., Brox, T.: Discriminative unsupervised feature learning with convolutional neural networks. In: NeurIPS (2014)
2014
Earlier work this paper cites.
Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: ICCV (2015)
2015
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: ICLR (2015)
2015
Earlier work this paper cites.
Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML deep learning workshop. vol. 2 (2015)
2015
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. International Journal of Computer Vision (IJCV) (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Earlier work this paper cites.
Larsson, G., Maire, M., Shakhnarovich, G.: Learning representations for automatic colorization. In: ECCV (2016)
2016
Earlier work this paper cites.
Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: ECCV (2016)
2016
Earlier work this paper cites.
Pathak, D., Krähenbühl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: Feature learning by inpainting. In: CVPR (2016)
2016
Earlier work this paper cites.
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. In: NeurIPS (2016)
2016
Earlier work this paper cites.
Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: ECCV (2016)
2016
Earlier work this paper cites.
Bojanowski, P., Joulin, A.: Unsupervised learning by predicting noise. In: ICML (2017)
2017
Earlier work this paper cites.
Doersch, C., Zisserman, A.: Multi-task self-supervised visual learning. In: ICCV (2017)
2017
Earlier work this paper cites.
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: ICML (2017)
2017
Earlier work this paper cites.
Kokkinos, I.: Ubernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory. In: CVPR (2017)
2017
Earlier work this paper cites.
Noroozi, M., Pirsiavash, H., Favaro, P.: Representation learning by learning to count. In: ICCV (2017)
2017
Earlier work this paper cites.
Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning. In: ICLR (2017)
2017
Earlier work this paper cites.
Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: NeurIPS (2017)
2017
Earlier work this paper cites.
Su, J.C., Maji, S.: Adapting models to signal degradation using distillation. In: BMVC (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Caron, M., Bojanowski, P., Joulin, A., Douze, M.: Deep clustering for unsupervised learning of visual features. In: ECCV (2018)
2018
Cited alongside, same era.
Chen, Z., Badrinarayanan, V., Lee, C.Y., Rabinovich, A.: Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks. In: ICML (2018)
2018
Cited alongside, same era.
Cui, Y., Song, Y., Sun, C., Howard, A., Belongie, S.: Large scale fine-grained categorization and domain-specific transfer learning. In: CVPR (2018)
2018
Achille, A., Lam, M., Tewari, R., Ravichandran, A., Maji, S., Fowlkes, C., Soatto, S., Perona, P.: Task2Vec: Task embedding for meta-learning. In: ICCV (2019)
2019
Closest in time.
2019
Closest in time.
Bertinetto, L., Henriques, J.F., Torr, P.H., Vedaldi, A.: Meta-learning with differentiable closed-form solvers. In: ICLR (2019)
2019
Closest in time.
Carlucci, F.M., D’Innocente, A., Bucci, S., Caputo, B., Tommasi, T.: Domain generalization by solving jigsaw puzzles. In: CVPR (2019)
2019
Closest in time.
Caron, M., Bojanowski, P., Mairal, J., Joulin, A.: Unsupervised pre-training of image features on non-curated data. In: ICCV (2019)
2019
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Cited alongside, same era.
Ghiasi, G., Lin, T.Y., Le, Q.V.: Dropblock: A regularization method for convolutional networks. In: NeurIPS (2018)
2018
Cited alongside, same era.
Gidaris, S., Komodakis, N.: Dynamic few-shot visual learning without forgetting. In: CVPR (2018)
2018
Cited alongside, same era.
Gidaris, S., Singh, P., Komodakis, N.: Unsupervised representation learning by predicting image rotations. In: ICLR (2018)
2018
Cited alongside, same era.
Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: CVPR (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Closest in time.
Chen, W.Y., Liu, Y.C., Kira, Z., Wang, Y.C., Huang, J.B.: A closer look at few-shot classification. In: ICLR (2019)
2019
Closest in time.
Gidaris, S., Bursuc, A., Komodakis, N., Pérez, P., Cord, M.: Boosting few-shot visual learning with self-supervision. In: ICCV (2019)
2019
Closest in time.
Goyal, P., Mahajan, D., Gupta, A., Misra, I.: Scaling and benchmarking self-supervised visual representation learning. In: ICCV (2019)
2019
Closest in time.
2019
Closest in time.
Hjelm, R.D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., Bengio, Y.: Learning deep representations by mutual information estimation and maximization. In: ICLR (2019)
2019
Closest in time.
Kolesnikov, A., Zhai, X., Beyer, L.: Revisiting self-supervised visual representation learning. In: CVPR (2019)
2019
Closest in time.
Lee, K., Maji, S., Ravichandran, A., Soatto, S.: Meta-learning with differentiable convex optimization. In: CVPR (2019)
2019
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Maninis, K.K., Radosavovic, I., Kokkinos, I.: Attentive single-tasking of multiple tasks. In: CVPR (2019)
2019
Closest in time.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: PyTorch: An imperative style, high-performance deep learning library. In: NeurIPS (2019)
2019
Closest in time.
2019
Closest in time.
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2019
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Zhai, X., Oliver, A., Kolesnikov, A., Beyer, L.: S4 L
2019
Closest in time.
Asano, Y.M., Rupprecht, C., Vedaldi, A.: A critical analysis of self-supervision, or what we can learn from a single image. In: ICLR (2020)
2020
Closest in time.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: ICML (2020)
2020
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He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: CVPR (2020)
2020
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Misra, I., van der Maaten, L.: Self-supervised learning of pretext-invariant representations. In: CVPR (2020)
2020
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Tian, Y., Krishnan, D., Isola, P.: Contrastive multiview coding. In: ECCV (2020)
2020
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Wallace, B., Hariharan, B.: Extending and analyzing self-supervised learning across domains. In: ECCV (2020)
2020
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