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Few-shot learning aims to classify unseen classes with a few training examples.
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P. Chaudhari, A. Choromanska, S. Soatto, Y. LeCun, C. Baldassi, C. Borgs, J. Chayes, L. Sagun, R. Zecchina, Entropy-sgd: Biasing gradient descent into wide valleys, in: International Conference on Learning Representations, 2017, pp. 1–19
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B. Neyshabur, S. Bhojanapalli, D. McAllester, N. Srebro, Exploring generalization in deep learning, in: Advances in Neural Information Processing Systems, 2017, pp. 5947–5956
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S. Ravi, H. Larochelle, Optimization as a model for few-shot learning, in: International Conference on Learning Representations, 2017, pp. 1–11
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S. Thulasidasan, G. Chennupati, J. Bilmes, T. Bhattacharya, S. Michalak, On mixup training: Improved calibration and predictive uncertainty for deep neural networks, in: Advances in Neural Information Processing Systems, 2019, pp. 13888–13899
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H. Li, D. Eigen, S. Dodge, M. Zeiler, X. Wang, Finding task-relevant features for few-shot learning by category traversal, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 1–10
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V. Verma, A. Lamb, C. Beckham, A. Najafi, I. Mitliagkas, A. Courville, D. Lopez-Paz, Y. Bengio, Manifold mixup: Better representations by interpolating hidden states, in: Proceedings of International Conference on Machine Learning, 2018, pp. 6438–6447
2018
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Y. Zhang, T. Xiang, T. M. Hospedales, H. Lu, Deep mutual learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 4320–4328
2018
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F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, T. M. Hospedales, Learning to compare: Relation network for few-shot learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1199–1208
2018
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B. Oreshkin, P. R. López, A. Lacoste, Tadam: Task dependent adaptive metric for improved few-shot learning, in: Advances in Neural Information Processing Systems, 2018, pp. 721–731
2018
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S. Gidaris, N. Komodakis, Dynamic few-shot visual learning without forgetting, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 4367–4375
2018
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H. Qi, M. Brown, D. G. Lowe, Low-shot learning with imprinted weights, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 5822–5830
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H. Zhang, M. Cisse, Y. N. Dauphin, D. Lopez-Paz, mixup: Beyond empirical risk minimization, in: International Conference on Learning Representations, 2018, pp. 1–13
2018
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Y. Tokozume, Y. Ushiku, T. Harada, Between-class learning for image classification, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 5486–5494
2018
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2019
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Q. Sun, Y. Liu, T.-S. Chua, B. Schiele, Meta-transfer learning for few-shot learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 403–412
2019
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S. Gidaris, N. Komodakis, Generating classification weights with gnn denoising autoencoders for few-shot learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 21–30
2019
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R. Müller, S. Kornblith, G. Hinton, When does label smoothing help?, in: Advances in Neural Information Processing Systems, 2019, pp. 4694–4703
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D. Sun, A. Yao, A. Zhou, H. Zhao, Deeply-supervised knowledge synergy, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 6997–7006
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W.-Y. Chen, Y.-C. Liu, Z. Kira, Y.-C. F. Wang, J.-B. Huang, A closer look at few-shot classification, in: International Conference on Learning Representations, 2019, pp. 1–16
2019
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K. Lee, S. Maji, A. Ravichandran, S. Soatto, Meta-learning with differentiable convex optimization, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 10657–10665
2019
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C. Xing, N. Rostamzadeh, B. Oreshkin, P. O. Pinheiro, Adaptive cross-modal few-shot learning, in: Advances in Neural Information Processing Systems, 2019, pp. 4848–4858
2019
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R. Hou, H. Chang, M. Bingpeng, S. Shan, X. Chen, Cross attention network for few-shot classification, in: Advances in Neural Information Processing Systems, 2019, pp. 4005–4016
2019
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H. Zhu, H. Liu, C. Zhu, Z. Deng, X. Sun, Learning spatial-temporal deformable networks for unconstrained face alignment and tracking in videos, Pattern Recognition (2020)
2020
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Z. Cheng, X. Zhu, S. Gong, Face re-identification challenge: Are face recognition models good enough?, Pattern Recognition (2020)
2020
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Y. Liu, X. Gao, Q. Gao, J. Han, L. Shao, Label-activating framework for zero-shot learning, Neural Networks 121 (2020) 1–9
2020
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J. Ukita, Causal importance of low-level feature selectivity for generalization in image recognition, Neural Networks 125 (2020) 185–193
2020
Closest in time.