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Deep neural networks have been able to outperform humans in some cases like image recognition and image classification.
J. H. Hansen, A. Sangwan, A. Joglekar, A. E. Bulut, L. Kaushik, and C. Yu, “Fearless steps: Apollo-11 corpus advancements for speech technologies from earth to the moon,” in Proc. Interspeech 2018 , 2018, pp. 2758–2762. [Online]. Available: http://dx.doi.org/10.21437/Interspeech.2018-1942
1942
Earlier work this paper cites.
J. Schmidhuber, “Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook,” Ph.D. dissertation, Technische Universität München, 1987
1987
Earlier work this paper cites.
Y. Bengio, S. Bengio, and J. Cloutier, Learning a synaptic learning rule . Citeseer, 1990
1990
Earlier work this paper cites.
C. G. Atkeson, A. W. Moore, and S. Schaal, “Locally weighted learning,” in Lazy learning . Springer, 1997, pp. 11–73
1997
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
T. D. Alter and R. Basri, “Extracting salient curves from images: An analysis of the saliency network,” International Journal of Computer Vision , vol. 27, no. 1, pp. 51–69, 1998
1998
Earlier work this paper cites.
R. Vilalta and Y. Drissi, “A perspective view and survey of meta-learning,” Artificial intelligence review , vol. 18, no. 2, pp. 77–95, 2002
2002
Earlier work this paper cites.
L. Fe-Fei et al. , “A bayesian approach to unsupervised one-shot learning of object categories,” in Proceedings Ninth IEEE International Conference on Computer Vision . IEEE, 2003, pp. 1134–1141
2003
Earlier work this paper cites.
G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray, “Visual categorization with bags of keypoints,” in Workshop on statistical learning in computer vision, ECCV , vol. 1, no. 1-22. Prague, 2004, pp. 1–2
2004
Earlier work this paper cites.
A. Globerson and S. T. Roweis, “Metric learning by collapsing classes,” in Advances in neural information processing systems , 2006, pp. 451–458
2006
Earlier work this paper cites.
J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon, “Information-theoretic metric learning,” in Proceedings of the 24th international conference on Machine learning , 2007, pp. 209–216
2007
Earlier work this paper cites.
A. T. E.-I.-G. Marseille, “Canazei winter school “frontiers of economic inequlity”,” ATEIG Marseille , 2008
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on knowledge and data engineering , vol. 22, no. 10, pp. 1345–1359, 2009
2009
Earlier work this paper cites.
F. Perronnin, J. Sánchez, and T. Mensink, “Improving the fisher kernel for large-scale image classification,” in European conference on computer vision . Springer, 2010, pp. 143–156
2010
Earlier work this paper cites.
H. Jégou, M. Douze, C. Schmid, and P. Pérez, “Aggregating local descriptors into a compact image representation,” in 2010 IEEE computer society conference on computer vision and pattern recognition . IEEE, 2010, pp. 3304–3311
2010
Earlier work this paper cites.
T. Mikolov, A. Deoras, D. Povey, L. Burget, and J. Černockỳ, “Strategies for training large scale neural network language models,” in 2011 IEEE Workshop on Automatic Speech Recognition & Understanding . IEEE, 2011, pp. 196–201
2011
Earlier work this paper cites.
R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, “Natural language processing (almost) from scratch,” Journal of machine learning research , vol. 12, no. Aug, pp. 2493–2537, 2011
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath et al. , “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” IEEE Signal processing magazine , vol. 29, no. 6, pp. 82–97, 2012
2012
Earlier work this paper cites.
S. Thrun and L. Pratt, Learning to learn . Springer Science & Business Media, 2012
2012
Earlier work this paper cites.
J. Carreira, R. Caseiro, J. Batista, and C. Sminchisescu, “Semantic segmentation with second-order pooling,” in European Conference on Computer Vision . Springer, 2012, pp. 430–443
2012
Earlier work this paper cites.
T. N. Sainath, A.-r. Mohamed, B. Kingsbury, and B. Ramabhadran, “Deep convolutional neural networks for lvcsr,” in 2013 IEEE international conference on acoustics, speech and signal processing . IEEE, 2013, pp. 8614–8618
2013
Earlier work this paper cites.
Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE transactions on pattern analysis and machine intelligence , vol. 35, no. 8, pp. 1798–1828, 2013
2013
Earlier work this paper cites.
A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean, M. Ranzato, and T. Mikolov, “Devise: A deep visual-semantic embedding model,” in Advances in neural information processing systems , 2013, pp. 2121–2129
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2014, pp. 1725–1732
2014
Earlier work this paper cites.
J. Towns, T. Cockerill, M. Dahan, I. Foster, K. Gaither, A. Grimshaw, V. Hazlewood, S. Lathrop, D. Lifka, G. D. Peterson et al. , “Xsede: accelerating scientific discovery,” Computing in science & engineering , vol. 16, no. 5, pp. 62–74, 2014
2014
Earlier work this paper cites.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” in Advances in neural information processing systems , 2014, pp. 3320–3328
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
J. Weston, S. Chopra, and A. Bordes, “Memory networks,” arXiv preprint arXiv:1410.3916 , 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in neural information processing systems , 2014, pp. 3104–3112
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
V. Mnih, N. Heess, A. Graves et al. , “Recurrent models of visual attention,” in Advances in neural information processing systems , 2014, pp. 2204–2212
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , vol. 521, no. 7553, pp. 436–444, 2015
2015
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 1–9
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
C. A. Stewart, T. M. Cockerill, I. Foster, D. Hancock, N. Merchant, E. Skidmore, D. Stanzione, J. Taylor, S. Tuecke, G. Turner et al. , “Jetstream: a self-provisioned, scalable science and engineering cloud environment,” in Proceedings of the 2015 XSEDE Conference: Scientific Advancements Enabled by Enhanced Cyberinfrastructure , 2015, pp. 1–8
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
G. Koch, R. Zemel, and R. Salakhutdinov, “Siamese neural networks for one-shot image recognition,” in ICML deep learning workshop , vol. 2. Lille, 2015
2015
Earlier work this paper cites.
Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid, “Label-embedding for image classification,” IEEE transactions on pattern analysis and machine intelligence , vol. 38, no. 7, pp. 1425–1438, 2015
2015
Earlier work this paper cites.
Z. Akata, S. Reed, D. Walter, H. Lee, and B. Schiele, “Evaluation of output embeddings for fine-grained image classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 2927–2936
2015
Earlier work this paper cites.
T.-Y. Lin, A. RoyChowdhury, and S. Maji, “Bilinear cnn models for fine-grained visual recognition,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1449–1457
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko, “Simultaneous deep transfer across domains and tasks,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 4068–4076
2015
Earlier work this paper cites.
S. Sukhbaatar, J. Weston, R. Fergus et al. , “End-to-end memory networks,” in Advances in neural information processing systems , 2015, pp. 2440–2448
2015
Cited alongside, same era.
O. Vinyals, M. Fortunato, and N. Jaitly, “Pointer networks,” in Advances in neural information processing systems , 2015, pp. 2692–2700
2015
Cited alongside, same era.
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum, “Human-level concept learning through probabilistic program induction,” Science , vol. 350, no. 6266, pp. 1332–1338, 2015
2015
Cited alongside, same era.
Z. Zhang and V. Saligrama, “Zero-shot learning via semantic similarity embedding,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 4166–4174
2015
Cited alongside, same era.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al. , “Imagenet large scale visual recognition challenge,” International journal of computer vision , vol. 115, no. 3, pp. 211–252, 2015
X. Wang, W. Chen, J. Wu, Y.-F. Wang, and W. Yang Wang, “Video captioning via hierarchical reinforcement learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 4213–4222
2018
Later among the works it cites.
M. Lapan, Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more . Packt Publishing Ltd, 2018
2018
Later among the works it cites.
J. Pachocki, G. Brockman, J. Raiman, S. Zhang, H. Pondé, J. Tang, F. Wolski, C. Dennison, R. Jozefowicz, P. Debiak et al. , “Openai five, 2018,” URL https://blog. openai. com/openai-five , 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
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2015
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2818–2826
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Y.-X. Wang and M. Hebert, “Learning to learn: Model regression networks for easy small sample learning,” in European Conference on Computer Vision . Springer, 2016, pp. 616–634
2016
Cited alongside, same era.
L. Bertinetto, J. F. Henriques, J. Valmadre, P. Torr, and A. Vedaldi, “Learning feed-forward one-shot learners,” in Advances in neural information processing systems , 2016, pp. 523–531
2016
Cited alongside, same era.
Y. Xian, Z. Akata, G. Sharma, Q. Nguyen, M. Hein, and B. Schiele, “Latent embeddings for zero-shot classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 69–77
2016
Cited alongside, same era.
2016
Cited alongside, same era.
S. Gidaris and N. Komodakis, “Dynamic few-shot visual learning without forgetting,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 4367–4375
2018
Later among the works it cites.
H. Qi, M. Brown, and D. G. Lowe, “Low-shot learning with imprinted weights,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 5822–5830
2018
Later among the works it cites.
W.-H. Chu and Y.-C. F. Wang, “Learning semantics-guided visual attention for few-shot image classification,” in 2018 25th IEEE International Conference on Image Processing (ICIP) . IEEE, 2018, pp. 2979–2983
2018
Later among the works it cites.
Y. Xian, T. Lorenz, B. Schiele, and Z. Akata, “Feature generating networks for zero-shot learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 5542–5551
2018
Later among the works it cites.
Y.-X. Wang, R. Girshick, M. Hebert, and B. Hariharan, “Low-shot learning from imaginary data,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7278–7286
2018
Later among the works it cites.
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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 1199–1208
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
B. Oreshkin, P. R. López, and A. Lacoste, “Tadam: Task dependent adaptive metric for improved few-shot learning,” in Advances in Neural Information Processing Systems , 2018, pp. 721–731
2018
Later among the works it cites.
E. Schwartz, L. Karlinsky, J. Shtok, S. Harary, M. Marder, A. Kumar, R. Feris, R. Giryes, and A. Bronstein, “Delta-encoder: an effective sample synthesis method for few-shot object recognition,” in Advances in Neural Information Processing Systems , 2018, pp. 2845–2855
2018
Later among the works it cites.
D. Acharya, Z. Huang, D. Pani Paudel, and L. Van Gool, “Covariance pooling for facial expression recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2018, pp. 367–374
2018
Later among the works it cites.
Q. Cai, Y. Pan, T. Yao, C. Yan, and T. Mei, “Memory matching networks for one-shot image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4080–4088
2018
Later among the works it cites.
2018
Later among the works it cites.
J. H. Hansen, A. Joglekar, M. C. Shekhar, V. Kothapally, C. Yu, L. Kaushik, and A. Sangwan, “The 2019 Inaugural Fearless Steps Challenge: A Giant Leap for Naturalistic Audio,” in Proc. Interspeech 2019 , 2019, pp. 1851–1855. [Online]. Available: http://dx.doi.org/10.21437/Interspeech.2019-2301
2019
Later among the works it cites.
N. Ebadi, B. Lwowski, M. Jaloli, and P. Rad, “Implicit life event discovery from call transcripts using temporal input transformation network,” IEEE Access , vol. 7, pp. 172 178–172 189, 2019
2019
Later among the works it cites.
G. De La Torre, P. Rad, and K.-K. R. Choo, “Implementation of deep packet inspection in smart grids and industrial internet of things: Challenges and opportunities,” Journal of Network and Computer Applications , 2019
2019
Later among the works it cites.
S. H. Silva, P. Rad, N. Beebe, K.-K. R. Choo, and M. Umapathy, “Cooperative unmanned aerial vehicles with privacy preserving deep vision for real-time object identification and tracking,” Journal of Parallel and Distributed Computing , vol. 131, pp. 147–160, 2019
2019
Later among the works it cites.
H. Chacon, S. Silva, and P. Rad, “Deep learning poison data attack detection,” in 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) . IEEE, 2019, pp. 971–978
2019
Later among the works it cites.
Y. Wang, Q. Yao, J. Kwok, and L. M. Ni, “Generalizing from a few examples: A survey on few-shot learning,” in arXiv: 1904.05046 , 2019
2019
Later among the works it cites.
A. Alfassy, L. Karlinsky, A. Aides, J. Shtok, S. Harary, R. Feris, R. Giryes, and A. M. Bronstein, “Laso: Label-set operations networks for multi-label few-shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 6548–6557
2019
Later among the works it cites.
H. Zhang, J. Zhang, and P. Koniusz, “Few-shot learning via saliency-guided hallucination of samples,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 2770–2779
2019
Later among the works it cites.
W.-H. Chu, Y.-J. Li, J.-C. Chang, and Y.-C. F. Wang, “Spot and learn: A maximum-entropy patch sampler for few-shot image classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 6251–6260
2019
Later among the works it cites.
Z. Chen, Y. Fu, Y.-X. Wang, L. Ma, W. Liu, and M. Hebert, “Image deformation meta-networks for one-shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 8680–8689
2019
Later among the works it cites.
X. Wang, F. Yu, R. Wang, T. Darrell, and J. E. Gonzalez, “Tafe-net: Task-aware feature embeddings for low shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 1831–1840
2019
Later among the works it cites.
L. Karlinsky, J. Shtok, S. Harary, E. Schwartz, A. Aides, R. Feris, R. Giryes, and A. M. Bronstein, “Repmet: Representative-based metric learning for classification and few-shot object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 5197–5206
2019
Later among the works it cites.
Y. Pan, T. Yao, Y. Li, Y. Wang, C.-W. Ngo, and T. Mei, “Transferrable prototypical networks for unsupervised domain adaptation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 2239–2247
2019
Later among the works it cites.
D. Wertheimer and B. Hariharan, “Few-shot learning with localization in realistic settings,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 6558–6567
2019
Later among the works it cites.
M. A. Jamal and G.-J. Qi, “Task agnostic meta-learning for few-shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 11 719–11 727
2019
Later among the works it cites.
Q. Sun, Y. Liu, T.-S. Chua, and B. Schiele, “Meta-transfer learning for few-shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 403–412
2019
Later among the works it cites.
2019
Later among the works it cites.
E. Schonfeld, S. Ebrahimi, S. Sinha, T. Darrell, and Z. Akata, “Generalized zero-and few-shot learning via aligned variational autoencoders,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 8247–8255
2019
Later among the works it cites.
A. Li, T. Luo, Z. Lu, T. Xiang, and L. Wang, “Large-scale few-shot learning: Knowledge transfer with class hierarchy,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 7212–7220
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
——, “The omniglot challenge: a 3-year progress report,” Current Opinion in Behavioral Sciences , vol. 29, pp. 97–104, 2019
2019
Later among the works it cites.
C. Xing, N. Rostamzadeh, B. Oreshkin, and P. O. Pinheiro, “Adaptive cross-modal few-shot learning,” in Advances in Neural Information Processing Systems , 2019, pp. 4848–4858
2019
Later among the works it cites.
A. Razavi, A. van den Oord, and O. Vinyals, “Generating diverse high-fidelity images with vq-vae-2,” in Advances in Neural Information Processing Systems , 2019, pp. 14 866–14 876
2019
Later among the works it cites.
N. Bendre, N. Ebadi, J. J. Prevost, and P. Najafirad, “Human action performance using deep neuro-fuzzy recurrent attention model,” IEEE Access , vol. 8, pp. 57 749–57 761, 2020
2020
Closest in time.
2020
Closest in time.
G. D. L. T. Parra, P. Rad, K.-K. R. Choo, and N. Beebe, “Detecting internet of things attacks using distributed deep learning,” Journal of Network and Computer Applications , p. 102662, 2020
2020
Closest in time.
G. De La Torre, P. Rad, and K.-K. R. Choo, “Driverless vehicle security: Challenges and future research opportunities,” Future Generation Computer Systems , vol. 108, pp. 1092–1111, 2020
2020
Closest in time.
S. H. Silva, A. Alaeddini, and P. Najafirad, “Temporal graph traversals using reinforcement learning with proximal policy optimization,” IEEE Access , vol. 8, pp. 63 910–63 922, 2020
2020
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2020
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L. Jing and Y. Tian, “Self-supervised visual feature learning with deep neural networks: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020
2020
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L. Zhang, T. Xiang, and S. Gong, “Learning a deep embedding model for zero-shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 2021–2030
2030
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