, Prototype Matching Nets

&. Ours-cosine-classifier and . Avg, Weight Gen 45

C. Classifier and &. Att, Weight Gen 46

, with and without priors) fot the ImageNet based few-shot benchmark proposed in [4] (for more details about the evaluation metrics we refer to [26]). For each novel category we use N = 1, 2, 5, 10 or 20 training examples. Methods with " w/ H " use mechanisms that hallucinate extra training examples for the novel categories, The second rows in our entries report the 95% confidence intervals

M. Andrychowicz, M. Denil, S. Gomez, M. W. Hoffman, D. Pfau et al., Learning to learn by gradient descent by gradient descent, Advances in Neural Information Processing Systems, pp.3981-3989, 2016.

L. Chunjie and Y. Qiang, Cosine normalization: Using cosine similarity instead of dot product in neural networks, 2017.

C. Finn, P. Abbeel, and S. Levine, Model-agnostic metalearning for fast adaptation of deep networks. arXiv preprint, p.7, 2017.

B. Hariharan and R. Girshick, Low-shot visual recognition by shrinking and hallucinating features. arXiv preprint arXiv:1606, 2008.

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778, 2008.
DOI : 10.1109/CVPR.2016.90

S. Hochreiter and J. Schmidhuber, Long Short-Term Memory, Neural Computation, vol.4, issue.8, pp.1735-1780, 1997.
DOI : 10.1016/0893-6080(88)90007-X

E. Hoffer and N. Ailon, Deep Metric Learning Using Triplet Network, International Workshop on Similarity-Based Pattern Recognition, pp.84-92, 2015.
DOI : 10.1145/1553374.1553469
URL : http://arxiv.org/pdf/1412.6622

G. Koch, R. Zemel, and R. Salakhutdinov, Siamese neural networks for one-shot image recognition, ICML Deep Learning Workshop, 2015.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012.
DOI : 10.1162/neco.2009.10-08-881

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradientbased learning applied to document recognition, Proceedings of the IEEE, pp.2278-2324, 1998.

T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka, Metric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost, Computer Vision?ECCV 2012, pp.488-501, 2012.
DOI : 10.1007/978-3-642-33709-3_35
URL : https://hal.archives-ouvertes.fr/hal-00722313

N. Mishra, M. Rohaninejad, X. Chen, and P. Abbeel, Meta-learning with temporal convolutions. arXiv preprint, p.7, 2017.

T. Munkhdalai and H. Yu, Meta networks. arXiv preprint, 2017.

V. Nair and G. E. Hinton, Rectified linear units improve restricted boltzmann machines, Proceedings of the 27th international conference on machine learning (ICML-10), pp.807-814, 2010.

H. Qi, M. Brown, and D. G. Lowe, Learning with imprinted weights. arXiv preprint, 2017.

S. Ravi and H. Larochelle, Optimization as a model for fewshot learning, 2007.

S. Rebuffi, A. Kolesnikov, G. Sperl, and C. H. Lampert, iCaRL: Incremental Classifier and Representation Learning, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2001-2010, 2017.
DOI : 10.1109/CVPR.2017.587
URL : http://arxiv.org/pdf/1611.07725

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh et al., ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision, vol.1010, issue.1, pp.211-252, 2015.
DOI : 10.1007/978-3-642-15555-0_11
URL : http://dspace.mit.edu/bitstream/1721.1/104944/1/11263_2015_Article_816.pdf

A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap, One-shot learning with memory-augmented neural networks, 2016.

J. Schmidhuber, J. Zhao, and M. Wiering, Shifting inductive bias with success-story algorithm, adaptive levin search, and incremental self-improvement, Machine Learning, pp.105-130, 1997.

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint, 2014.

J. Snell, K. Swersky, and R. S. , Prototypical networks for few-shot learning. arXiv preprint, 2008.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed et al., Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-9, 2015.
DOI : 10.1109/CVPR.2015.7298594
URL : http://arxiv.org/pdf/1409.4842

S. Thrun, Lifelong learning algorithms. Learning to learn, pp.181-209, 1998.

O. Vinyals, C. Blundell, T. Lillicrap, and D. Wierstra, Matching networks for one shot learning, Advances in Neural Information Processing Systems, pp.3630-3638, 2008.

Y. Wang, R. Girshick, M. Hebert, and B. Hariharan, Low-shot learning from imaginary data, 2018.

B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva, Learning deep features for scene recognition using places database
DOI : 10.1109/tpami.2017.2723009

K. Q. Lawrence and . Weinberger, Advances in Neural Information Processing Systems 27, pp.487-495, 2014.