-
1
-
-
84906351367
-
Analyzing the performance of multilayer neural networks for object recognition
-
Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.), Springer, Heidelberg
-
Agrawal, P., Girshick, R., Malik, J.: Analyzing the performance of multilayer neural networks for object recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 329-344. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10584-0_22
-
(2014)
ECCV 2014. LNCS
, vol.8695
, pp. 329-344
-
-
Agrawal, P.1
Girshick, R.2
Malik, J.3
-
3
-
-
0001471775
-
Unsupervised learning
-
Barlow, H.B.: Unsupervised learning. Neural Comput. 1, 295-311 (1989)
-
(1989)
Neural Comput
, vol.1
, pp. 295-311
-
-
Barlow, H.B.1
-
4
-
-
0042378381
-
Laplacian eigenmaps for dimensionality reduction and data representation
-
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15, 1373-1396 (2003)
-
(2003)
Neural Comput
, vol.15
, pp. 1373-1396
-
-
Belkin, M.1
Niyogi, P.2
-
5
-
-
84879854889
-
Representation learning: A review and new perspectives
-
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. PAMI 35(8), 1798-1828 (2013)
-
(2013)
PAMI
, vol.35
, Issue.8
, pp. 1798-1828
-
-
Bengio, Y.1
Courville, A.2
Vincent, P.3
-
6
-
-
0024220237
-
Auto-association by multilayer perceptrons and singular value decomposition
-
Boulard, H., Kamp, Y.: Auto-association by multilayer perceptrons and singular value decomposition. Biol. Cybern. 59, 291-294 (1988)
-
(1988)
Biol. Cybern
, vol.59
, pp. 291-294
-
-
Boulard, H.1
Kamp, Y.2
-
7
-
-
80052910201
-
Cityscale landmark identification on mobile devices
-
Chen, D.M., Baatz, G., Koser, K., Tsai, S.S., Vedantham, R., Pylvanainen, T., Roimela, K., Chen, X., Bach, J., Pollefeys, M., Girod, B., Grzeszczuk, R.: Cityscale landmark identification on mobile devices. In: CVPR (2011)
-
(2011)
CVPR
-
-
Chen, D.M.1
Baatz, G.2
Koser, K.3
Tsai, S.S.4
Vedantham, R.5
Pylvanainen, T.6
Roimela, K.7
Chen, X.8
Bach, J.9
Pollefeys, M.10
Girod, B.11
Grzeszczuk, R.12
-
8
-
-
85198028989
-
Imagenet: A large-scale hierarchical image database
-
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR (2009)
-
(2009)
CVPR
-
-
Deng, J.1
Dong, W.2
Socher, R.3
Li, L.J.4
Li, K.5
Fei-Fei, L.6
-
9
-
-
84973916088
-
Unsupervised visual representation learning by context prediction
-
Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: ICCV (2015)
-
(2015)
ICCV
-
-
Doersch, C.1
Gupta, A.2
Efros, A.A.3
-
10
-
-
84919881041
-
Decaf: A deep convolutional activation feature for generic visual recognition
-
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: a deep convolutional activation feature for generic visual recognition. In: ICML (2014)
-
(2014)
ICML
-
-
Donahue, J.1
Jia, Y.2
Vinyals, O.3
Hoffman, J.4
Zhang, N.5
Tzeng, E.6
Darrell, T.7
-
11
-
-
84952007662
-
The pascal visual object classes challenge: A retrospective
-
Everingham, M., Eslami, S.M.A., Gool, L.V., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes challenge: A retrospective. IJCV (2014)
-
(2014)
IJCV
-
-
Everingham, M.1
Eslami, S.M.A.2
Gool, L.V.3
Williams, C.K.I.4
Winn, J.5
Zisserman, A.6
-
12
-
-
0001252371
-
Apictorial jigsaw puzzles: The computer solution of a problem in pattern recognition
-
Freeman, H., Garder, L.: Apictorial jigsaw puzzles: the computer solution of a problem in pattern recognition. IEEE Trans. Electron. Comput. EC-13, 118-127 (1964)
-
(1964)
IEEE Trans. Electron. Comput
, vol.EC-13
, pp. 118-127
-
-
Freeman, H.1
Garder, L.2
-
13
-
-
84911400494
-
Rich feature hierarchies for accurate object detection and semantic segmentation
-
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR (2014)
-
(2014)
CVPR
-
-
Girshick, R.1
Donahue, J.2
Darrell, T.3
Malik, J.4
-
14
-
-
85029359197
-
Fast r-cnn
-
Girshick, R.: Fast r-cnn. In: ICCV (2015)
-
(2015)
ICCV
-
-
Girshick, R.1
-
15
-
-
84922907906
-
High-speed tracking with kernelized correlation filters
-
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. PAMI (2015)
-
(2015)
PAMI
-
-
Henriques, J.F.1
Caseiro, R.2
Martins, P.3
Batista, J.4
-
17
-
-
0002834189
-
Autoencoders, minimum description length and helmholtz free energy
-
Hinton, G.E., Zemel, R.S.: Autoencoders, minimum description length and helmholtz free energy. NIPS (1993)
-
(1993)
NIPS
-
-
Hinton, G.E.1
Zemel, R.S.2
-
18
-
-
0003918810
-
The Hooper Visual Organization Test
-
Los Angeles
-
Hooper, H.: The Hooper Visual Organization Test. Western Psychological Services, Los Angeles (1983)
-
(1983)
Western Psychological Services
-
-
Hooper, H.1
-
19
-
-
84969584486
-
Batch normalization: Accelerating deep network training by reducing internal covariate shift
-
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML (2015)
-
(2015)
ICML
-
-
Ioffe, S.1
Szegedy, C.2
-
20
-
-
84990045295
-
Understanding neural networks through deep visualization
-
Jason, Y., Jeff, C., Anh, N., Thomas, F., Hod, L.: Understanding neural networks through deep visualization. In: Deep Learning Workshop, ICML (2015)
-
(2015)
Deep Learning Workshop, ICML
-
-
Jason, Y.1
Jeff, C.2
Anh, N.3
Thomas, F.4
Hod, L.5
-
21
-
-
84913580146
-
Caffe: Convolutional architecture for fast feature embedding
-
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. ACM-MM (2014)
-
(2014)
ACM-MM
-
-
Jia, Y.1
Shelhamer, E.2
Donahue, J.3
Karayev, S.4
Long, J.5
Girshick, R.6
Guadarrama, S.7
Darrell, T.8
-
22
-
-
85083952350
-
Data-dependent initializations of convolutional neural networks
-
Krähenbühl, P., Doersch, C., Donahue, J., Darrell, T.: Data-dependent initializations of convolutional neural networks. In: ICLR (2016)
-
(2016)
ICLR
-
-
Krähenbühl, P.1
Doersch, C.2
Donahue, J.3
Darrell, T.4
-
23
-
-
84876231242
-
Imagenet classification with deep convolutional neural networks
-
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. NIPS (2012)
-
(2012)
NIPS
-
-
Krizhevsky, A.1
Sutskever, I.2
Hinton, G.E.3
-
24
-
-
84867135575
-
Building high-level features using large scale unsupervised learning
-
Le, Q., Ranzato, M., Monga, R., Devin, M., Chen, K., Corrado, G., Dean, J., Ng, A.: Building high-level features using large scale unsupervised learning. In: ICML (2012)
-
(2012)
ICML
-
-
Le, Q.1
Ranzato, M.2
Monga, R.3
Devin, M.4
Chen, K.5
Corrado, G.6
Dean, J.7
Ng, A.8
-
25
-
-
84959213675
-
Understanding deep image representations by inverting them
-
Mahendran, A., Vedaldi, A.: Understanding deep image representations by inverting them. In: CVPR (2015)
-
(2015)
CVPR
-
-
Mahendran, A.1
Vedaldi, A.2
-
26
-
-
0030779611
-
Sparse coding with an overcomplete basis set: A strategy employed by v1?
-
Olshausen, B.A., Field, D.J.: Sparse coding with an overcomplete basis set: a strategy employed by v1? Vision Research (1997)
-
(1997)
Vision Research
-
-
Olshausen, B.A.1
Field, D.J.2
-
27
-
-
84986294165
-
Context encoders: Feature learning by inpainting
-
Pathak, D., Krähenbühl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: CVPR (2016)
-
(2016)
CVPR
-
-
Pathak, D.1
Krähenbühl, P.2
Donahue, J.3
Darrell, T.4
Efros, A.A.5
-
28
-
-
80052904077
-
A fully automated greedy square jigsaw puzzle solver
-
Pomeranz, D., Shemesh, M., Ben-Shahar, O.: A fully automated greedy square jigsaw puzzle solver. In: CVPR (2011)
-
(2011)
CVPR
-
-
Pomeranz, D.1
Shemesh, M.2
Ben-Shahar, O.3
-
32
-
-
85083953896
-
Deep inside convolutional networks: Visualising image classification models and saliency maps
-
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: Visualising image classification models and saliency maps. In: ICLR (2014)
-
(2014)
ICLR
-
-
Simonyan, K.1
Vedaldi, A.2
Zisserman, A.3
-
33
-
-
84937862424
-
Two-stream convolutional networks for action recognition in videos
-
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. NIPS (2014)
-
(2014)
NIPS
-
-
Simonyan, K.1
Zisserman, A.2
-
34
-
-
0000329993
-
Information processing in dynamical systems: Foundations of harmony theory
-
Smolensky, P.: Information processing in dynamical systems: Foundations of harmony theory. Parallel Distributed Processing (1986)
-
(1986)
Parallel Distributed Processing
-
-
Smolensky, P.1
-
36
-
-
84973889989
-
Unsupervised learning of visual representations using videos
-
Wang, X., Gupta, A.: Unsupervised learning of visual representations using videos. In: ICCV (2015)
-
(2015)
ICCV
-
-
Wang, X.1
Gupta, A.2
-
37
-
-
84937508363
-
How transferable are features in deep neural networks?
-
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? NIPS (2014)
-
(2014)
NIPS
-
-
Yosinski, J.1
Clune, J.2
Bengio, Y.3
Lipson, H.4
-
38
-
-
84906489074
-
Visualizing and understanding convolutional networks
-
Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.), Springer, Heidelberg
-
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818-833. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10590-1_53
-
(2014)
ECCV 2014. LNCS
, vol.8689
, pp. 818-833
-
-
Zeiler, M.D.1
Fergus, R.2
|