Unsupervised learning on neural network outputs: With application in zero-shot learning

IJCAI International Joint Conference on Artificial Intelligence

Volumn 2016-January, Issue , 2016, Pages 3432-3438

  Lu, Yao ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); LEARNING SYSTEMS; NEURAL NETWORKS; UNSUPERVISED LEARNING;

CONVOLUTIONAL NEURAL NETWORK; HIDDEN STRUCTURES; LEARNING METHODS; OBJECT CLASS; STATE OF THE ART; TRAINED NEURAL NETWORKS; VISUAL FEATURE; VISUAL SIMILARITY;

LEARNING ALGORITHMS;

EID: 85006110312     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. Zeynep Akata, Florent Perronnin, Zaid Harchaoui, and Cordelia Schmid. Label-embedding for attribute-based classification. CVPR, 2013.
2. M Asuncion Vicente, Patrik O Hoyer, and Aapo Hyvärinen. Equivalence ofsome common linear feature extraction techniques for appearance-based object recognition tasks. TPAMI, 2007.
3. Carlo Baldassi, Alireza Alemi-Neissi, Marino Pagan, James J DiCarlo, Riccardo Zecchina, and Davide Zoccolan. Shape similarity, better than semantic membership, accounts for the structure of visual object representations in a population of monkey inferotemporal neurons. PLoS Computational Biology, 2013.
4. James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley, and Yoshua Bengio. Theano: A CPU and GPU math expression compiler. SciPy, 2010.
5. Charles F Cadieu, Ha Hong, Daniel LK Yamins, Nicolas Pinto, Diego Ardila, Ethan A Solomon, Najib J Majaj, and James J DiCarlo. Deep neural networks rival the representation of primate it cortex for core visual object recognition. PLoS Computational Biology, 2014.
6. Dan Ciresan, Ueli Meier, and Jürgen Schmidhuber. Multi-column deep neural networks for image classification. CVPR, 2012.
7. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. CVPR, 2009.
8. Thomas Deselaers and Vittorio Ferrari. Visual and semantic similarity in imagenet. CVPR, 2011.
9. Christiane Fellbaum. WordNet: An Electronic Lexical Database. 1998.
10. Andrea Frome, Greg S Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Tomas Mikolov, et al. Devise: A deep visual-semantic embedding model. NIPS, 2013.
11. Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR, 2014.
12. David Hardoon, Sandor Szedmak, and John Shawe-Taylor. Canonical correlation analysis: An overview with application to learning methods. Neural Computation, 2004.
13. Geoffrey E Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. NIPS Deep Learning Workshop, 2014.
14. Harold Hotelling. Relations between two sets of variates. Biometrika, 1936.
15. Aapo Hyvärinen, Juha Karhunen, and Erkki Oja. Independent Component Analysis. 2004.
16. Aapo Hyvarinen. Fast and robust fixedpoint algorithms for independent component analysis. TNN, 1999.
17. Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. Caffe: Convolutional architecture for fast feature embedding. ArXiv: 1408. 5093, 2014.
18. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. NIPS, 2012.
19. Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling. Learning to detect unseen object classes by between-class attribute transfer. CVPR, 2009.
20. Hugo Larochelle, Dumitru Erhan, and Yoshua Bengio. Zero-data learning of new tasks. AAAI, 2008.
21. Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998.
22. Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. CVPR, 2015.
23. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. ICLR, 2013.
24. Mohammad Norouzi, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg S Corrado, and Jeffrey Dean. Zero-shot learning by convex combination of semantic embeddings. ICLR, 2014.
25. Mark Palatucci, Dean Pomerleau, Geoffrey E Hinton, and Tom M Mitchell. Zero-shot learning with semantic output codes. NIPS, 2009.
26. Ross Girshick Jian Sun Shaoqing Ren, Kaiming He. Faster R-CNN: Towards real-time object detection with region proposal networks. NIPS, 2015.
27. Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. CVPR, 2015.
28. Srinivas C Turaga, Joseph F Murray, Viren Jain, Fabian Roth, Moritz Helmstaedter, Kevin Briggman, Winfried Denk, and H Sebastian Seung. Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Computation, 2010.