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Volumn , Issue , 2018, Pages 7278-7286

Low-Shot Learning from Imaginary Data

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[No Author keywords available]

Indexed keywords

BENCHMARKING;

EID: 85062884597     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2018.00760     Document Type: Conference Paper
Times cited : (841)

References (39)
  • 1
    • 33745139169 scopus 로고    scopus 로고
    • Cross-generalization: Learning novel classes from a single example by feature replacement
    • E. Bart and S. Ullman. Cross-generalization: Learning novel classes from a single example by feature replacement. In CVPR, 2005.
    • (2005) CVPR
    • Bart, E.1    Ullman, S.2
  • 3
    • 85041895800 scopus 로고    scopus 로고
    • An empirical study and analysis of generalized zero-shot learning for object recognition in the wild
    • W.-L. Chao, S. Changpinyo, B. Gong, and F. Sha. An empirical study and analysis of generalized zero-shot learning for object recognition in the wild. In ECCV, 2016.
    • (2016) ECCV
    • Chao, W.-L.1    Changpinyo, S.2    Gong, B.3    Sha, F.4
  • 6
    • 85112095815 scopus 로고    scopus 로고
    • Towards a neural statistician
    • H. Edwards and A. Storkey. Towards a neural statistician. In ICLR, 2017.
    • (2017) ICLR
    • Edwards, H.1    Storkey, A.2
  • 7
    • 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, 2006.
    • (2006) TPAMI
    • Fei-Fei, L.1    Fergus, R.2    Perona, P.3
  • 8
    • 84898963788 scopus 로고    scopus 로고
    • Object classification from a single example utilizing class relevance metrics
    • M. Fink. Object classification from a single example utilizing class relevance metrics. NIPS, 2005.
    • (2005) NIPS
    • Fink, M.1
  • 9
    • 85041899497 scopus 로고    scopus 로고
    • Model-agnostic metalearning for fast adaptation of deep networks
    • C. Finn, P. Abbeel, and S. Levine. Model-agnostic metalearning for fast adaptation of deep networks. In ICML, 2017.
    • (2017) ICML
    • Finn, C.1    Abbeel, P.2    Levine, S.3
  • 12
    • 33845594569 scopus 로고    scopus 로고
    • Dimensionality reduction by learning an invariant mapping
    • R. Hadsell, S. Chopra, and Y. LeCun. Dimensionality reduction by learning an invariant mapping. In CVPR, 2006.
    • (2006) CVPR
    • Hadsell, R.1    Chopra, S.2    LeCun, Y.3
  • 13
    • 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
  • 14
    • 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
  • 15
    • 85083952489 scopus 로고    scopus 로고
    • Auto-encoding variational Bayes
    • D. P. Kingma and M. Welling. Auto-encoding variational Bayes. In ICLR, 2014.
    • (2014) ICLR
    • Kingma, D.P.1    Welling, M.2
  • 17
    • 84898998554 scopus 로고    scopus 로고
    • Oneshot learning by inverting a compositional causal process
    • B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. Oneshot learning by inverting a compositional causal process. In NIPS. 2013.
    • (2013) NIPS.
    • Lake, B.M.1    Salakhutdinov, R.2    Tenenbaum, J.B.3
  • 18
    • 84949683101 scopus 로고    scopus 로고
    • Humanlevel concept learning through probabilistic program induction
    • B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. Humanlevel concept learning through probabilistic program induction. Science, 2015.
    • (2015) Science
    • Lake, B.M.1    Salakhutdinov, R.2    Tenenbaum, J.B.3
  • 20
    • 0033712893 scopus 로고    scopus 로고
    • Learning from one example through shared densities on transforms
    • E. G. Miller, N. E. Matsakis, and P. A. Viola. Learning from one example through shared densities on transforms. In CVPR, 2000.
    • (2000) CVPR
    • Miller, E.G.1    Matsakis, N.E.2    Viola, P.A.3
  • 21
    • 33845573438 scopus 로고    scopus 로고
    • Incremental learning of object detectors using a visual shape alphabet
    • A. Opelt, A. Pinz, and A. Zisserman. Incremental learning of object detectors using a visual shape alphabet. In CVPR, 2006.
    • (2006) CVPR
    • Opelt, A.1    Pinz, A.2    Zisserman, A.3
  • 22
    • 85083950271 scopus 로고    scopus 로고
    • Unsupervised representation learning with deep convolutional generative adversarial networks
    • A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. In ICLR, 2016.
    • (2016) ICLR
    • Radford, A.1    Metz, L.2    Chintala, S.3
  • 23
    • 85041901997 scopus 로고    scopus 로고
    • Optimization as a model for fewshot learning
    • S. Ravi and H. Larochelle. Optimization as a model for fewshot learning. In ICLR, 2017.
    • (2017) ICLR
    • Ravi, S.1    Larochelle, H.2
  • 29
    • 84946751287 scopus 로고    scopus 로고
    • FaceNet: A unified embedding for face recognition and clustering
    • F. Schroff, D. Kalenichenko, and J. Philbin. FaceNet: A unified embedding for face recognition and clustering. In CVPR, 2015.
    • (2015) CVPR
    • Schroff, F.1    Kalenichenko, D.2    Philbin, J.3
  • 30
    • 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
  • 31
    • 84959194885 scopus 로고    scopus 로고
    • Web-scale training for face identification
    • Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Web-scale training for face identification. In CVPR, 2015.
    • (2015) CVPR
    • Taigman, Y.1    Yang, M.2    Ranzato, M.3    Wolf, L.4
  • 32
    • 0041494227 scopus 로고    scopus 로고
    • Is learning the n-th thing any easier than learning the first?
    • S. Thrun. Is learning the n-th thing any easier than learning the first? NIPS, 1996.
    • (1996) NIPS
    • Thrun, S.1
  • 33
    • 0010687621 scopus 로고    scopus 로고
    • Lifelong learning algorithms
    • S. Thrun. Lifelong learning algorithms. Learning to learn, 8:181-209, 1998.
    • (1998) Learning to Learn , vol.8 , pp. 181-209
    • Thrun, S.1
  • 34
    • 57249084011 scopus 로고    scopus 로고
    • Visualizing data using t-SNE
    • L. van der Maaten and G. Hinton. Visualizing data using t-SNE. JMLR, 9:2579-2605, 2008.
    • (2008) JMLR , vol.9 , pp. 2579-2605
    • Van Der Maaten, L.1    Hinton, G.2
  • 36
    • 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
  • 37
    • 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
  • 39
    • 84973910975 scopus 로고    scopus 로고
    • One shot learning via compositions of meaningful patches
    • A. Wong and A. L. Yuille. One shot learning via compositions of meaningful patches. In ICCV, 2015.
    • (2015) ICCV
    • Wong, A.1    Yuille, A.L.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.