-
1
-
-
84986259967
-
Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks
-
S. Bell, C. L. Zitnick, K. Bala, and R. Girshick. Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks. In CVPR, 2016.
-
(2016)
CVPR
-
-
Bell, S.1
Zitnick, C.L.2
Bala, K.3
Girshick, R.4
-
5
-
-
33645146449
-
Histograms of oriented gradients for human detection
-
N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005.
-
(2005)
CVPR
-
-
Dalal, N.1
Triggs, B.2
-
6
-
-
84937896655
-
Exploiting linear structure within convolutional networks for efficient evaluation
-
E. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus. Exploiting linear structure within convolutional networks for efficient evaluation. In NIPS, 2014.
-
(2014)
NIPS
-
-
Denton, E.1
Zaremba, W.2
Bruna, J.3
LeCun, Y.4
Fergus, R.5
-
7
-
-
84919881041
-
Decaf: A deep convolutional activation feature for generic visual recognition
-
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. 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
-
9
-
-
85029359197
-
Fast R-CNN
-
R. Girshick. Fast R-CNN. In ICCV, 2015.
-
(2015)
ICCV
-
-
Girshick, R.1
-
10
-
-
84911400494
-
Rich feature hierarchies for accurate object detection and semantic segmentation
-
R. Girshick, J. Donahue, T. Darrell, and J. Malik. 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
-
13
-
-
84973911419
-
Delving deep into rectifiers: Surpassing human-level performance on ima-genet classification
-
K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into rectifiers: Surpassing human-level performance on ima-genet classification. In ICCV, 2015.
-
(2015)
ICCV
-
-
He, K.1
Zhang, X.2
Ren, S.3
Sun, J.4
-
14
-
-
84986274465
-
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
-
-
84990056336
-
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
-
17
-
-
84969584486
-
Batch normalization: Accelerating deep network training by reducing internal covariate shift
-
S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML, 2015.
-
(2015)
ICML
-
-
Ioffe, S.1
Szegedy, C.2
-
18
-
-
85062833929
-
Speeding up convolutional neural networks with low rank expansions
-
M. Jaderberg, A. Vedaldi, and A. Zisserman. Speeding up convolutional neural networks with low rank expansions. In BMVC, 2014.
-
(2014)
BMVC
-
-
Jaderberg, M.1
Vedaldi, A.2
Zisserman, A.3
-
19
-
-
84913555165
-
-
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. arXiv:1408.5093, 2014.
-
(2014)
Caffe: Convolutional Architecture for Fast Feature Embedding
-
-
Jia, Y.1
Shelhamer, E.2
Donahue, J.3
Karayev, S.4
Long, J.5
Girshick, R.6
Guadarrama, S.7
Darrell, T.8
-
20
-
-
85021676739
-
-
N. Kalchbrenner, L. Espeholt, K. Simonyan, A. V. D. Oord, A. Graves, and K. Kavukcuoglu. Neural machine translation in linear time. arXiv:1610.10099, 2016.
-
(2016)
Neural Machine Translation in Linear Time
-
-
Kalchbrenner, N.1
Espeholt, L.2
Simonyan, K.3
Oord, A.V.D.4
Graves, A.5
Kavukcuoglu, K.6
-
21
-
-
85083951289
-
Compression of deep convolutional neural networks for fast and low power mobile applications
-
Y.-D. Kim, E. Park, S. Yoo, T. Choi, L. Yang, and D. Shin. Compression of deep convolutional neural networks for fast and low power mobile applications. In ICLR, 2016.
-
(2016)
ICLR
-
-
Kim, Y.-D.1
Park, E.2
Yoo, S.3
Choi, T.4
Yang, L.5
Shin, D.6
-
23
-
-
77956002520
-
Learning multiple layers of features from tiny images
-
A. Krizhevsky. Learning multiple layers of features from tiny images. Tech Report, 2009.
-
(2009)
Tech Report
-
-
Krizhevsky, A.1
-
24
-
-
84876231242
-
Imagenet classification with deep convolutional neural networks
-
A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
-
(2012)
NIPS
-
-
Krizhevsky, A.1
Sutskever, I.2
Hinton, G.3
-
25
-
-
0000359337
-
Backpropagation applied to handwritten zip code recognition
-
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural computation, 1989.
-
(1989)
Neural Computation
-
-
LeCun, Y.1
Boser, B.2
Denker, J.S.3
Henderson, D.4
Howard, R.E.5
Hubbard, W.6
Jackel, L.D.7
-
27
-
-
84937834115
-
Microsoft COCO: Common objects in context
-
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. Microsoft COCO: Common objects in context. In ECCV. 2014.
-
(2014)
ECCV
-
-
Lin, T.-Y.1
Maire, M.2
Belongie, S.3
Hays, J.4
Perona, P.5
Ramanan, D.6
Dollár, P.7
Zitnick, C.L.8
-
28
-
-
84959205572
-
Fully convolutional networks for semantic segmentation
-
J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015.
-
(2015)
CVPR
-
-
Long, J.1
Shelhamer, E.2
Darrell, T.3
-
29
-
-
3042535216
-
Distinctive image features from scale-invariant keypoints
-
D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 2004.
-
(2004)
IJCV
-
-
Lowe, D.G.1
-
30
-
-
85011070895
-
-
A. Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu. Wavenet: A generative model for raw audio. arXiv:1609.03499, 2016.
-
(2016)
Wavenet: A Generative Model for Raw Audio
-
-
Oord, A.1
Dieleman, S.2
Zen, H.3
Simonyan, K.4
Vinyals, O.5
Graves, A.6
Kalchbrenner, N.7
Senior, A.8
Kavukcuoglu, K.9
-
32
-
-
84960980241
-
Faster R-CNN: Towards real-time object detection with region proposal networks
-
S. Ren, K. He, R. Girshick, and J. Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. In NIPS, 2015.
-
(2015)
NIPS
-
-
Ren, S.1
He, K.2
Girshick, R.3
Sun, J.4
-
33
-
-
84947041871
-
Imagenet large scale visual recognition challenge
-
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. IJCV, 2015.
-
(2015)
IJCV
-
-
Russakovsky, O.1
Deng, J.2
Su, H.3
Krause, J.4
Satheesh, S.5
Ma, S.6
Huang, Z.7
Karpathy, A.8
Khosla, A.9
Bernstein, M.10
Berg, A.C.11
Fei-Fei, L.12
-
34
-
-
85083951635
-
Overfeat: Integrated recognition, localization and detection using convolutional networks
-
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. In ICLR, 2014.
-
(2014)
ICLR
-
-
Sermanet, P.1
Eigen, D.2
Zhang, X.3
Mathieu, M.4
Fergus, R.5
LeCun, Y.6
-
36
-
-
85083953063
-
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
-
37
-
-
84983383396
-
Inception-v4, inception-resnet and the impact of residual connections on learning
-
C. Szegedy, S. Ioffe, and V. Vanhoucke. Inception-v4, inception-resnet and the impact of residual connections on learning. In ICLR Workshop, 2016.
-
(2016)
ICLR Workshop
-
-
Szegedy, C.1
Ioffe, S.2
Vanhoucke, V.3
-
38
-
-
84937522268
-
Going deeper with convolutions
-
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabi-novich. Going deeper with convolutions. In CVPR, 2015.
-
(2015)
CVPR
-
-
Szegedy, C.1
Liu, W.2
Jia, Y.3
Sermanet, P.4
Reed, S.5
Anguelov, D.6
Erhan, D.7
Vanhoucke, V.8
Rabi-Novich, A.9
-
39
-
-
84986296808
-
Rethinking the inception architecture for computer vision
-
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wo-jna. Rethinking the inception architecture for computer vision. In CVPR, 2016.
-
(2016)
CVPR
-
-
Szegedy, C.1
Vanhoucke, V.2
Ioffe, S.3
Shlens, J.4
Wo-Jna, Z.5
-
40
-
-
85019250516
-
Residual networks behave like ensembles of relatively shallow network
-
A. Veit, M. Wilber, and S. Belongie. Residual networks behave like ensembles of relatively shallow network. In NIPS, 2016.
-
(2016)
NIPS
-
-
Veit, A.1
Wilber, M.2
Belongie, S.3
-
41
-
-
85018271332
-
-
Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey, et al. Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv:1609.08144, 2016.
-
(2016)
Google'S Neural Machine Translation System: Bridging The Gap between Human and Machine Translation
-
-
Wu, Y.1
Schuster, M.2
Chen, Z.3
Le, Q.V.4
Norouzi, M.5
Macherey, W.6
Krikun, M.7
Cao, Y.8
Gao, Q.9
Macherey, K.10
-
42
-
-
85006844471
-
-
W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, and G. Zweig. The Microsoft 2016 Conversational Speech Recognition System. arXiv:1609.03528, 2016.
-
(2016)
The Microsoft 2016 Conversational Speech Recognition System
-
-
Xiong, W.1
Droppo, J.2
Huang, X.3
Seide, F.4
Seltzer, M.5
Stolcke, A.6
Yu, D.7
Zweig, G.8
-
44
-
-
84937902251
-
Visualizing and understanding convolutional neural networks
-
M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional neural networks. In ECCV, 2014.
-
(2014)
ECCV
-
-
Zeiler, M.D.1
Fergus, R.2
|