Becoming the expert - Interactive multi-class machine teaching

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 07-12-June-2015, Issue , 2015, Pages 2616-2624

  Johns, Edward ^a   Aodha, Oisin Mac ^a   Brostow, Gabriel J ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE ALGORITHMS; PATTERN RECOGNITION; STUDENTS;

IMAGE IMPORTANCE; LABELED IMAGES; PRIOR INFORMATION; PRIOR KNOWLEDGE; REAL-WORLD DATASETS; TEACHING ALGORITHMS; TEACHING STRATEGY; VISUAL CONCEPT;

COMPUTER VISION;

EID: 84959192539     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7298877     Document Type: Conference Paper

References (46)

1. Amazon Mechanical Turk. https://www. mturk. com, 2014
2. Django Web Framework. https://www. djangoproject. com, 2014
3. F. J. Balbach and T. Zeugmann. Recent developments in algorithmic teaching. In Language and Automata Theory and Applications. 2009
4. S. Basu and J. Christensen. Teaching classification boundaries to humans. In AAAI, 2013
5. Y. Bengio, J. Louradour, R. Collobert, and J. Weston. Curriculum learning. In ICML, 2009
6. T. Berg and P. N. Belhumeur. How do you tell a blackbird from a crow? In ICCV, 2013
7. J. S. Bruner. The Process of Education. Harvard University Press, 1960
8. M. Cakmak and M. Lopes. Algorithmic and human teaching of sequential decision tasks. In AAAI, 2012
9. M. J. Crump, J. V. McDonnell, and T. M. Gureckis. Evaluating Amazon's Mechanical Turk as a tool for experimental behavioral research. PloS one, 2013
10. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005
11. J. Deng, J. Krause, and L. Fei-Fei. Fine-grained crowdsourcing for fine-grained recognition. In CVPR, 2013
12. A. Deza and D. Parikh. Understanding image virality. In CVPR, 2015
13. T. Doliwa, H. U. Simon, and S. Zilles. Recursive teaching dimension, learning complexity, and maximum classes. In Algorithmic Learning Theory, 2010
14. J. Du and C. X. Ling. Active teaching for inductive learners. In SDM, 2011
15. S. Ebert, M. Fritz, and B. Schiele. RALF: A Reinforced Active Learning Formulation for Object Class Recognition. In CVPR, 2012
16. M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes (VOC) challenge. IJCV, 2010
17. G. Giguère and B. C. Love. Limits in decision making arise from limits in memory retrieval. PNAS, 110(19):7613-7618, 2013
18. S. A. Goldman and M. J. Kearns. On the complexity of teaching. Journal of Computer and System Sciences, 1992
19. G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. 2007
20. P. Isola, J. Xiao, D. Parikh, A. Torralba, and A. Oliva. What makes a photograph memorable? PAMI, 2013
21. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. arXiv:1408. 5093, 2014
22. A. J. Joshi, F. Porikli, and N. Papanikolopoulos. Breaking the interactive bottleneck in multi-class classification with active selection and binary feedback. In CVPR, 2010
23. B. Kulis. Metric Learning: A Survey. Foundations & Trends in Machine Learning, 2012
24. N. Kumar, P. N. Belhumeur, A. Biswas, D. W. Jacobs, W. J. Kress, I. C. Lopez, and J. V. Soares. Leafsnap: A computer vision system for automatic plant species identification. In ECCV, 2012
25. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollr, and C. Zitnick. Microsoft COCO: Common Objects in Context. In ECCV, 2014
26. C.-L. Liu, F. Yin, D.-H. Wang, and Q.-F. Wang. Casia online and offline chinese handwriting databases. ICDAR, 2011
27. C. Long, G. Hua, and A. Kapoor. Active visual recognition with expertise estimation in crowdsourcing. In ICCV, 2013
28. B. C. Love. Categorization. In Oxford Handbook of Cognitive Neuroscience, pages 342-358. 2013
29. O. Mac Aodha, N. D. Campbell, J. Kautz, and G. J. Brostow. Hierarchical Subquery Evaluation for Active Learning on a Graph. In CVPR, 2014
30. O. Mac Aodha, V. Stathopoulos, G. J. Brostow, M. Terry, M. Girolami, and K. E. Jones. Putting the scientist in the loop-accelerating scientific progress with interactive machine learning. In ICPR, 2014
31. D. L. Medin and M. M. Schaffer. Context theory of classification learning. Psychological review, 85(3):207, 1978
32. P. O'Donovan, J. Lbeks, A. Agarwala, and A. Hertzmann. Exploratory font selection using crowdsourced attributes. ACM Transactions on Graphics (TOG), 2014
33. K. R. Patil, X. Zhu, . Kopéc, and B. C. Love. Optimal teaching for limited-capacity human learners. In NIPS, 2014
34. V. C. Raykar, S. Yu, L. H. Zhao, A. Jerebko, C. Florin, G. H. Valadez, L. Bogoni, and L. Moy. Supervised learning from multiple experts: whom to trust when everyone lies a bit. In ICML, 2009
35. J. J. Richler and T. J. Palmeri. Visual category learning. Wiley Interdisciplinary Reviews: Cognitive Science, 2014
36. N. Roy and A. McCallum. Toward optimal active learning through sampling estimation of error reduction. In ICML, 2001
37. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. Imagenet large scale visual recognition challenge. arXiv:1409. 0575, 2014
38. B. Settles. Active Learning. Morgan & Claypool, 2012
39. A. Singla, I. Bogunovic, G. Bartók, A. Karbasi, and A. Krause. Near-optimally teaching the crowd to classify. In ICML, 2014
40. P. Welinder, S. Branson, S. Belongie, and P. Perona. The multidimensional wisdom of crowds. In NIPS, 2010
41. D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf. Learning with local and global consistency. In NIPS, 2004
42. X. Zhu. Machine teaching for Bayesian learners in the exponential family. In NIPS, 2013
43. X. Zhu. Machine teaching: An inverse problem to machine learning and an approach toward optimal education. AAAI Conference on Artificial Intelligence (Senior Member Track), 2015
44. X. Zhu, Z. Ghahramani, and J. Lafferty. Semi-supervised learning using Gaussian fields and harmonic functions. In ICML, 2003
45. X. Zhu, J. Lafferty, and Z. Ghahramani. Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions. In ICML workshops, 2003
46. S. Zilles, S. Lange, R. Holte, and M. Zinkevich. Models of cooperative teaching and learning. JMLR, 2011.