Tadam: Task dependent adaptive metric for improved few-shot learning

Advances in Neural Information Processing Systems

Volumn 2018-December, Issue , 2018, Pages 721-731

  Oreshkin, Boris N ^a   Rodriguez, Pau ^b   Lacoste, Alexandre ^a  

^a ServiceNow Element AI   (Canada)

^b Barcelona Perception Computing Lab   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

SET THEORY; TOPOLOGY;

ADAPTIVE METRIC; ALGORITHM PARAMETERS; CLASSIFICATION TASKS; CO-TRAINING; LEARNING MODELS; METRIC SPACES; OPTIMIZATION PROCEDURES; STATE OF THE ART;

IMAGE ENHANCEMENT;

EID: 85064429112     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (34)

1. ´ atkowski, B. Schölkopf, and R. E. Turner. Discriminative k-shot learning using probabilistic models. arXiv preprint arXiv:1706.00326, 2017.
2. S. Carey and E. Bartlett. Acquiring a single new word. 1978.
3. V. Dumoulin, J. Shlens, and M. Kudlur. A learned representation for artistic style. ICLR, 2017.
4. H. Edwards and A. Storkey. Towards a neural statistician. arXiv preprint arXiv:1606.02185, 2016.
5. M. Fink. Object classification from a single example utilizing class relevance metrics. In NIPS, pages 449-456, 2005.
6. C. Finn, P. Abbeel, and S. Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML, pages 1126-1135, 2017.
7. R. Hadsell, S. Chopra, and Y. LeCun. Dimensionality reduction by learning an invariant mapping. In CVPR, volume 2, pages 1735-1742. IEEE, 2006.
8. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. CVPR, pages 770-778, 2016.
9. G. Hinton, O. Vinyals, and J. Dean. Distilling the knowledge in a neural network. In NIPS Deep Learning and Representation Learning Workshop, 2015. URL http://arxiv.org/abs/1503.02531.
10. G. Koch, R. Zemel, and R. Salakhutdinov. Siamese neural networks for one-shot image recognition. In ICML Deep Learning Workshop, volume 2, 2015.
11. A. Krizhevsky. Learning multiple layers of features from tiny images., University of Toronto, 2009.
12. A. Lacoste, T. Boquet, N. Rostamzadeh, B. Oreshkin, W. Chung, and D. Krueger. Deep prior. arXiv preprint arXiv:1712.05016, 2017.
13. B. M. Lake, R. R. Salakhutdinov, and J. Tenenbaum. One-shot learning by inverting a compositional causal process. In NIPS, pages 2526-2534, 2013.
14. B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. Human-level concept learning through probabilistic program induction. Science, 350(6266):1332-1338, 2015.
15. F.-F. Li, R. Fergus, and P. Perona. One-shot learning of object categories. PAMI, 28(4):594-611, 2006.
16. N. Mishra, M. Rohaninejad, X. Chen, and P. Abbeel. A simple neural attentive meta-learner. In ICLR, 2018.
17. T. Munkhdalai, X. Yuan, S. Mehri, and A. Trischler. Rapid adaptation with conditionally shifted neurons. In ICML, 2018.
18. E. Perez, H. de Vries, F. Strub, V. Dumoulin, and A. C. Courville. Learning visual reasoning without strong priors. CoRR, abs/1707.03017, 2017.
19. E. Perez, F. Strub, H. De Vries, V. Dumoulin, and A. Courville. Film: Visual reasoning with a general conditioning layer. In AAAI, 2018.
20. B. Plank and H. M. Alonso. When is multitask learning effective? Semantic sequence prediction under varying data conditions. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017, Valencia, Spain, pages 44-53, 2017.
21. P. Ramachandran, B. Zoph, and Q. V. Lea. Searching for activation functions. In ICLR, 2018.
22. S. Ravi and H. Larochelle. Optimization as a model for few-shot learning. In ICLR, 2016.
23. M. Ren, E. Triantafillou, S. Ravi, J. Snell, K. Swersky, J. B. Tenenbaum, H. Larochelle, and R. S. Zemel. Meta-learning for semi-supervised few-shot classification. arXiv preprint arXiv:1803.00676, 2018.
24. A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap. Meta-learning with memory-augmented neural networks. In M. F. Balcan and K. Q. Weinberger, editors, ICML, volume 48 of Proceedings of Machine Learning Research, pages 1842-1850, New York, New York, USA, 20-22 Jun 2016. PMLR.
25. J. Schmidhuber, J. Zhao, and M. Wiering. Shifting inductive bias with success-story algorithm, adaptive levin search, and incremental self-improvement. Machine Learning, 28(1):105-130, 1997.
26. F. Schroff, D. Kalenichenko, and J. Philbin. Facenet: A unified embedding for face recognition and clustering. In CVPR, pages 815-823, 2015.
27. P. Shyam, S. Gupta, and A. Dukkipati. Attentive recurrent comparators. In ICML, pages 3173-3181, 2017.
28. J. Snell, K. Swersky, and R. S. Zemel. Prototypical networks for few-shot learning. In NIPS, pages 4080-4090, 2017.
29. F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales. Learning to compare: Relation network for few-shot learning. In CVPR, 2018.
30. Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Web-scale training for face identification. In CVPR, pages 2746-2754, 2015.
31. S. Thrun. Lifelong learning algorithms. In Learning to learn, pages 181-209. Springer, 1998.
32. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin. Attention is all you need. In NIPS, pages 6000-6010, 2017.
33. O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and D. Wierstra. Matching networks for one shot learning. In NIPS, pages 3630-3638. 2016.
34. Y.-X. Wang, R. Girshick, M. Hebert, and B. Hariharan. Low-Shot Learning from Imaginary Data. In CVPR, 2018.