Fetching the paper…
Reading the bibliography…
We present a class of efficient models called MobileNets for mobile and embedded vision applications.
IM2GPS: estimating geographic information from a single image
J. Hays and A. Efros · 2008
Earlier work this paper cites.
Novel dataset for fine-grained image categorization
A. Khosla, N. Jayadevaprakash, B. Yao, and L. Fei-Fei · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
T. Tieleman and G. Hinton · 2012
Earlier work this paper cites.
Training deep neural networks with low precision multiplications
M. Courbariaux, J.-P. David, and Y. Bengio · 2014
Earlier work this paper cites.
Large-Scale Image Geolocalization
J. Hays and A. Efros · 2014
Earlier work this paper cites.
Speeding up convolutional neural networks with low rank expansions
M. Jaderberg, A. Vedaldi, and A. Zisserman · 2014
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Earlier work this paper cites.
Flattened convolutional neural networks for feedforward acceleration
J. Jin, A. Dundar, and E. Culurciello · 2014
Earlier work this paper cites.
Speeding-up convolutional neural networks using fine-tuned cp-decomposition
V. Lebedev, Y. Ganin, M. Rakhuba, I. Oseledets, and V. Lempitsky · 2014
Earlier work this paper cites.
Rigid-motion scattering for image classification
L. Sifre · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Tensorflow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al · 2015
Earlier work this paper cites.
Compressing neural networks with the hashing trick
W. Chen, J. T. Wilson, S. Tyree, K. Q. Weinberger, and Y. Chen · 2015
Cited alongside, same era.
S. Han, H. Mao, and W. J. Dally · 2015
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2015
Later among the works it cites.
Quantized convolutional neural networks for mobile devices
J. Wu, C. Leng, Y. Wang, Q. Hu, and J. Cheng · 2015
Later among the works it cites.
Deep fried convnets
Z. Yang, M. Moczulski, M. Denil, N. de Freitas, A. Smola, L. Song, and Z. Wang · 2015
Later among the works it cites.
Xception: Deep learning with depthwise separable convolutions
F. Chollet · 2016
Later among the works it cites.
Speed/accuracy trade-offs for modern convolutional object detectors
J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, et al · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
The unreasonable effectiveness of noisy data for fine-grained recognition
J. Krause, B. Sapp, A. Howard, H. Zhou, A. Toshev, T. Duerig, J. Philbin, and L. Fei-Fei · 2015
Cited alongside, same era.
Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, and S. Reed · 2015
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
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, et al · 2015
Cited alongside, same era.
Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
Cited alongside, same era.
Structured transforms for small-footprint deep learning
V. Sindhwani, T. Sainath, and S. Kumar · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
Later among the works it cites.
Quantized neural networks: Training neural networks with low precision weights and activations
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
Later among the works it cites.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 1mb model size
F. N. Iandola, M. W. Moskewicz, K. Ashraf, S. Han, W. J. Dally, and K. Keutzer · 2016
Later among the works it cites.
Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
Later among the works it cites.
Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, and V. Vanhoucke · 2016
Later among the works it cites.
Yfcc100m: The new data in multimedia research
B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L.-J. Li · 2016
Later among the works it cites.
Factorized convolutional neural networks
M. Wang, B. Liu, and H. Foroosh · 2016
Later among the works it cites.
PlaNet - Photo Geolocation with Convolutional Neural Networks
T. Weyand, I. Kostrikov, and J. Philbin · 2016
Later among the works it cites.