메뉴 건너뛰기




Volumn 2017-December, Issue , 2017, Pages 7030-7040

Learning to model the tail

Author keywords

[No Author keywords available]

Indexed keywords

CLASS DISTRIBUTIONS; CLASSIFICATION DATASETS; IMBALANCED DATA-SETS; LEARNING TO LEARN; META-KNOWLEDGE; MODEL PARAMETERS; REAL WORLD SETTING; TRANSFER LEARNING;

EID: 85047007460     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (690)

References (64)
  • 1
    • 84876231242 scopus 로고    scopus 로고
    • ImageNet classification with deep convolutional neural networks
    • A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, 2012.
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 2
    • 85083953063 scopus 로고    scopus 로고
    • Very deep convolutional networks for large-scale image recognition
    • K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015.
    • (2015) ICLR
    • Simonyan, K.1    Zisserman, A.2
  • 4
    • 84986274465 scopus 로고    scopus 로고
    • Deep residual learning for image recognition
    • K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016.
    • (2016) CVPR
    • He, K.1    Zhang, X.2    Ren, S.3    Sun, J.4
  • 8
    • 84911449311 scopus 로고    scopus 로고
    • Capturing long-tail distributions of object subcategories
    • X. Zhu, D. Anguelov, and D. Ramanan. Capturing long-tail distributions of object subcategories. In CVPR, 2014.
    • (2014) CVPR
    • Zhu, X.1    Anguelov, D.2    Ramanan, D.3
  • 9
    • 84924598740 scopus 로고    scopus 로고
    • Do we need more training data?
    • X. Zhu, C. Vondrick, C. C. Fowlkes, and D. Ramanan. Do we need more training data? IJCV, 119(1): 76-92, 2016.
    • (2016) IJCV , vol.119 , Issue.1 , pp. 76-92
    • Zhu, X.1    Vondrick, C.2    Fowlkes, C.C.3    Ramanan, D.4
  • 13
    • 84986302036 scopus 로고    scopus 로고
    • Factors in finetuning deep model for object detection with long-tail distribution
    • W. Ouyang, X. Wang, C. Zhang, and X. Yang. Factors in finetuning deep model for object detection with long-tail distribution. In CVPR, 2016.
    • (2016) CVPR
    • Ouyang, W.1    Wang, X.2    Zhang, C.3    Yang, X.4
  • 14
    • 84976407509 scopus 로고    scopus 로고
    • SUN database: Exploring a large collection of scene categories
    • J. Xiao, K. A. Ehinger, J. Hays, A. Torralba, and A. Oliva. SUN database: Exploring a large collection of scene categories. IJCV, 119(1): 3-22, 2016.
    • (2016) IJCV , vol.119 , Issue.1 , pp. 3-22
    • Xiao, J.1    Ehinger, K.A.2    Hays, J.3    Torralba, A.4    Oliva, A.5
  • 15
    • 85047017886 scopus 로고    scopus 로고
    • Sharing representations for long tail computer vision problems
    • S. Bengio. Sharing representations for long tail computer vision problems. In ICMI, 2015.
    • (2015) ICMI
    • Bengio, S.1
  • 16
    • 85044520154 scopus 로고    scopus 로고
    • Relay backpropagation for effective learning of deep convolutional neural networks
    • L. Shen, Z. Lin, and Q. Huang. Relay backpropagation for effective learning of deep convolutional neural networks. In ECCV, 2016.
    • (2016) ECCV
    • Shen, L.1    Lin, Z.2    Huang, Q.3
  • 18
    • 77956031473 scopus 로고    scopus 로고
    • A survey on transfer learning
    • S. J. Pan and Q. Yang. A survey on transfer learning. TKDE, 22(10): 1345-1359, 2010.
    • (2010) TKDE , vol.22 , Issue.10 , pp. 1345-1359
    • Pan, S.J.1    Yang, Q.2
  • 19
    • 84937508363 scopus 로고    scopus 로고
    • How transferable are features in deep neural networks?
    • J. Yosinski, J. Clune, Y. Bengio, and H. Lipson. How transferable are features in deep neural networks? In NIPS, 2014.
    • (2014) NIPS
    • Yosinski, J.1    Clune, J.2    Bengio, Y.3    Lipson, H.4
  • 20
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • G. E. Hinton and R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313(5786): 504-507, 2006.
    • (2006) Science , vol.313 , Issue.5786 , pp. 504-507
    • Hinton, G.E.1    Salakhutdinov, R.R.2
  • 21
    • 85018929847 scopus 로고    scopus 로고
    • Learning to learn: Model regression networks for easy small sample learning
    • Y.-X. Wang and M. Hebert. Learning to learn: Model regression networks for easy small sample learning. In ECCV, 2016.
    • (2016) ECCV
    • Wang, Y.-X.1    Hebert, M.2
  • 23
    • 85018930461 scopus 로고    scopus 로고
    • Learning from small sample sets by combining unsupervised meta-training with CNNs
    • Y.-X. Wang and M. Hebert. Learning from small sample sets by combining unsupervised meta-training with CNNs. In NIPS, 2016.
    • (2016) NIPS
    • Wang, Y.-X.1    Hebert, M.2
  • 24
    • 85088230669 scopus 로고    scopus 로고
    • Learning to optimize
    • K. Li and J. Malik. Learning to optimize. In ICLR, 2017.
    • (2017) ICLR
    • Li, K.1    Malik, J.2
  • 25
    • 85041901997 scopus 로고    scopus 로고
    • Optimization as a model for few-shot learning
    • S. Ravi and H. Larochelle. Optimization as a model for few-shot learning. In ICLR, 2017.
    • (2017) ICLR
    • Ravi, S.1    Larochelle, H.2
  • 26
    • 85047000737 scopus 로고    scopus 로고
    • Introspection: Accelerating neural network training by learning weight evolution
    • A. Sinha, M. Sarkar, A. Mukherjee, and B. Krishnamurthy. Introspection: Accelerating neural network training by learning weight evolution. In ICLR, 2017.
    • (2017) ICLR
    • Sinha, A.1    Sarkar, M.2    Mukherjee, A.3    Krishnamurthy, B.4
  • 27
    • 68549133155 scopus 로고    scopus 로고
    • Learning from imbalanced data
    • H. He and E. A. Garcia. Learning from imbalanced data. TKDE, 21(9): 1263-1284, 2009.
    • (2009) TKDE , vol.21 , Issue.9 , pp. 1263-1284
    • He, H.1    Garcia, E.A.2
  • 28
    • 84986295253 scopus 로고    scopus 로고
    • Learning deep representation for imbalanced classification
    • C. Huang, Y. Li, C. C. Loy, and X. Tang. Learning deep representation for imbalanced classification. In CVPR, 2016.
    • (2016) CVPR
    • Huang, C.1    Li, Y.2    Loy, C.C.3    Tang, X.4
  • 30
    • 0031186687 scopus 로고    scopus 로고
    • Shifting inductive bias with success-story algorithm, adaptive levin search, and incremental self-improvement
    • 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.
    • (1997) Machine Learning , vol.28 , Issue.1 , pp. 105-130
    • Schmidhuber, J.1    Zhao, J.2    Wiering, M.3
  • 31
    • 0031189914 scopus 로고    scopus 로고
    • Multitask learning
    • R. Caruana. Multitask learning. Machine Learning, 28(1): 41-75, 1997.
    • (1997) Machine Learning , vol.28 , Issue.1 , pp. 41-75
    • Caruana, R.1
  • 33
    • 0346377064 scopus 로고
    • Learning to control fast-weight memories: An alternative to dynamic recurrent networks
    • J. Schmidhuber. Learning to control fast-weight memories: An alternative to dynamic recurrent networks. Neural Computation, 4(1): 131-139, 1992.
    • (1992) Neural Computation , vol.4 , Issue.1 , pp. 131-139
    • Schmidhuber, J.1
  • 37
    • 85041899497 scopus 로고    scopus 로고
    • Model-agnostic meta-learning for fast adaptation of deep networks
    • C. Finn, P. Abbeel, and S. Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML, 2017.
    • (2017) ICML
    • Finn, C.1    Abbeel, P.2    Levine, S.3
  • 38
    • 85047006619 scopus 로고    scopus 로고
    • Learning multiple visual domains with residual adapters
    • S.-A. Rebuffi, H. Bilen, and A. Vedaldi. Learning multiple visual domains with residual adapters. In NIPS, 2017.
    • (2017) NIPS
    • Rebuffi, S.-A.1    Bilen, H.2    Vedaldi, A.3
  • 40
    • 84898938559 scopus 로고    scopus 로고
    • Zero-shot learning through cross-modal transfer
    • R. Socher, M. Ganjoo, C. D. Manning, and A. Ng. Zero-shot learning through cross-modal transfer. In NIPS, 2013.
    • (2013) NIPS
    • Socher, R.1    Ganjoo, M.2    Manning, C.D.3    Ng, A.4
  • 41
    • 84973882857 scopus 로고    scopus 로고
    • Predicting deep zero-shot convolutional neural networks using textual descriptions
    • J. Ba, K. Swersky, S. Fidler, and R. Salakhutdinov. Predicting deep zero-shot convolutional neural networks using textual descriptions. In ICCV, 2015.
    • (2015) ICCV
    • Ba, J.1    Swersky, K.2    Fidler, S.3    Salakhutdinov, R.4
  • 42
    • 84986261711 scopus 로고    scopus 로고
    • Image question answering using convolutional neural network with dynamic parameter prediction
    • H. Noh, P. H. Seo, and B. Han. Image question answering using convolutional neural network with dynamic parameter prediction. In CVPR, 2016.
    • (2016) CVPR
    • Noh, H.1    Seo, P.H.2    Han, B.3
  • 43
    • 33144466753 scopus 로고    scopus 로고
    • One-shot learning of object categories
    • L. Fei-Fei, R. Fergus, and P. Perona. One-shot learning of object categories. TPAMI, 28(4): 594-611, 2006.
    • (2006) TPAMI , vol.28 , Issue.4 , pp. 594-611
    • Fei-Fei, L.1    Fergus, R.2    Perona, P.3
  • 44
    • 84959250182 scopus 로고    scopus 로고
    • Model recommendation: Generating object detectors from few samples
    • Y.-X. Wang and M. Hebert. Model recommendation: Generating object detectors from few samples. In CVPR, 2015.
    • (2015) CVPR
    • Wang, Y.-X.1    Hebert, M.2
  • 45
    • 85020183301 scopus 로고    scopus 로고
    • Siamese neural networks for one-shot image recognition
    • G. Koch, R. Zemel, and R. Salakhutdinov. Siamese neural networks for one-shot image recognition. In ICML Workshops, 2015.
    • (2015) ICML Workshops
    • Koch, G.1    Zemel, R.2    Salakhutdinov, R.3
  • 46
    • 84949683101 scopus 로고    scopus 로고
    • Human-level concept learning through probabilistic program induction
    • B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. Human-level concept learning through probabilistic program induction. Science, 350(6266): 1332-1338, 2015.
    • (2015) Science , vol.350 , Issue.6266 , pp. 1332-1338
    • Lake, B.M.1    Salakhutdinov, R.2    Tenenbaum, J.B.3
  • 48
    • 85007242857 scopus 로고    scopus 로고
    • Learning by transferring from unsupervised universal sources
    • Y.-X. Wang and M. Hebert. Learning by transferring from unsupervised universal sources. In AAAI, 2016.
    • (2016) AAAI
    • Wang, Y.-X.1    Hebert, M.2
  • 49
    • 84994641616 scopus 로고    scopus 로고
    • Learning without forgetting
    • Z. Li and D. Hoiem. Learning without forgetting. In ECCV, 2016.
    • (2016) ECCV
    • Li, Z.1    Hoiem, D.2
  • 50
    • 85041907438 scopus 로고    scopus 로고
    • Low-shot visual recognition by shrinking and hallucinating features
    • B. Hariharan and R. Girshick. Low-shot visual recognition by shrinking and hallucinating features. In ICCV, 2017.
    • (2017) ICCV
    • Hariharan, B.1    Girshick, R.2
  • 53
    • 85046993347 scopus 로고    scopus 로고
    • Prototypical networks for few-shot learning
    • J. Snell, K. Swersky, and R. S. Zemel. Prototypical networks for few-shot learning. In NIPS, 2017.
    • (2017) NIPS
    • Snell, J.1    Swersky, K.2    Zemel, R.S.3
  • 54
    • 85040681540 scopus 로고    scopus 로고
    • Recent advances in zero-shot recognition: Toward data-efficient understanding of visual content
    • Y. Fu, T. Xiang, Y.-G. Jiang, X. Xue, L. Sigal, and S. Gong. Recent advances in zero-shot recognition: Toward data-efficient understanding of visual content. IEEE Signal Processing Magazine, 35(1): 112-125, 2018.
    • (2018) IEEE Signal Processing Magazine , vol.35 , Issue.1 , pp. 112-125
    • Fu, Y.1    Xiang, T.2    Jiang, Y.-G.3    Xue, X.4    Sigal, L.5    Gong, S.6
  • 56
    • 85046992622 scopus 로고    scopus 로고
    • Few-shot learning through an information retrieval lens
    • E. Triantafillou, R. Zemel, and R. Urtasun. Few-shot learning through an information retrieval lens. In NIPS, 2017.
    • (2017) NIPS
    • Triantafillou, E.1    Zemel, R.2    Urtasun, R.3
  • 57
    • 84990056336 scopus 로고    scopus 로고
    • Identity mappings in deep residual networks
    • K. He, X. Zhang, S. Ren, and J. Sun. Identity mappings in deep residual networks. In ECCV, 2016.
    • (2016) ECCV
    • He, K.1    Zhang, X.2    Ren, S.3    Sun, J.4
  • 58
    • 85041923826 scopus 로고    scopus 로고
    • Revisiting unreasonable effectiveness of data in deep learning era
    • C. Sun, A. Shrivastava, S. Singh, and A. Gupta. Revisiting unreasonable effectiveness of data in deep learning era. In ICCV, 2017.
    • (2017) ICCV
    • Sun, C.1    Shrivastava, A.2    Singh, S.3    Gupta, A.4
  • 61
    • 84973389608 scopus 로고    scopus 로고
    • Analyzing the performance of multilayer neural networks for object recognition
    • P. Agrawal, R. Girshick, and J. Malik. Analyzing the performance of multilayer neural networks for object recognition. In ECCV, 2014.
    • (2014) ECCV
    • Agrawal, P.1    Girshick, R.2    Malik, J.3
  • 62
    • 85020207136 scopus 로고    scopus 로고
    • What makes ImageNet good for transfer learning?
    • M. Huh, P. Agrawal, and A. A. Efros. What makes ImageNet good for transfer learning? In NIPS workshops, 2016.
    • (2016) NIPS Workshops
    • Huh, M.1    Agrawal, P.2    Efros, A.A.3
  • 63
    • 85044381962 scopus 로고    scopus 로고
    • Growing a brain: Fine-tuning by increasing model capacity
    • Y.-X. Wang, D. Ramanan, and M. Hebert. Growing a brain: Fine-tuning by increasing model capacity. In CVPR, 2017.
    • (2017) CVPR
    • Wang, Y.-X.1    Ramanan, D.2    Hebert, M.3
  • 64
    • 57249084011 scopus 로고    scopus 로고
    • Visualizing data using t-SNE
    • L. van der Maaten and G. Hinton. Visualizing data using t-SNE. JMLR, 9(Nov): 2579-2605, 2008.
    • (2008) JMLR , vol.9 , Issue.NOV , pp. 2579-2605
    • Van Der Maaten, L.1    Hinton, G.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.