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Transfer learning is a widely used method to build high performing computer vision models.
Improving predictive inference under covariate shift by weighting the log-likelihood function
H. Shimodaira · 2000
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Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure
M. Saerens, P. Latinne, and C. Decaestecker · 2002
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Direct importance estimation with model selection and its application to covariate shift adaptation
M. Sugiyama, S. Nakajima, H. Kashima, P. v. Bünau, and M. Kawanabe · 2007
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Automated flower classification over a large number of classes
M.-E. Nilsback and A. Zisserman · 2008
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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When training and test sets are different: Characterising learning transfer
A. J. Storkey · 2009
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Cats and dogs
O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. V. Jawahar · 2012
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Collecting a large-scale dataset of fine-grained cars
J. Krause, J. Deng, M. Stark, and L. Fei-Fei · 2013
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Fine-grained visual classification of aircraft
S. Maji, E. Rahtu, J. Kannala, M. B. Blaschko, and A. Vedaldi · 2013
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Domain adaptation under target and conditional shift
K. Zhang, B. Schölkopf, K. Muandet, and Z. Wang · 2013
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Analyzing the performance of multilayer neural networks for object recognition
P. Agrawal, R. B. Girshick, and J. Malik · 2014
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Birdsnap: Large-scale fine-grained visual categorization of birds
T. Berg, J. Liu, S. W. Lee, M. L. Alexander, D. W. Jacobs, and P. N. Belhumeur · 2014
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Food-101 - mining discriminative components with random forests
L. Bossard, M. Guillaumin, and L. J. V. Gool · 2014
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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 · 2014
Cited alongside, same era.
Cnn features off-the-shelf: An astounding baseline for recognition
A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson · 2014
Cited alongside, same era.
How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
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Fast r-cnn
R. Girshick · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. B. 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, A. C. Berg, and L. Fei-Fei · 2015
Speed/accuracy trade-offs for modern convolutional object detectors
J. Huang, V. Rathod, C. Sun, M. Zhu, A. K. Balan, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, and K. Murphy · 2017
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Fully convolutional networks for semantic segmentation
E. Shelhamer, J. Long, and T. Darrell · 2017
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Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
C. Sun, A. Shrivastava, S. Singh, and A. Gupta · 2017
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Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2017
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2018
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Cited alongside, same era.
Factors of transferability for a generic convnet representation
H. Azizpour, A. S. Razavian, J. Sullivan, A. Maki, and S. Carlsson · 2016
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 · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Cited alongside, same era.
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
Cited alongside, same era.
How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow, Keras & React Native, 2017
T. Anglade · 2017
Cited alongside, same era.
Borrowing treasures from the wealthy: Deep transfer learning through selective joint fine-tuning
W. Ge and Y. Yu · 2017
Cited alongside, same era.
Stanford DAWN Deep Learning Benchmark (DAWNBench), 2018
C. A. Coleman, D. Narayanan, D. Kang, T. Zhao, J. Zhang, L. Nardi, P. Bailis, K. Olukotun, C. Ré, and M. Zaharia · 2018
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Large scale fine-grained categorization and domain-specific transfer learning
Y. Cui, Y. Song, C. Sun, A. Howard, and S. Belongie · 2018
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Gpipe: Efficient training of giant neural networks using pipeline parallelism
Y. Huang, Y. Cheng, D. Chen, H. Lee, J. Ngiam, Q. V. Le, and Z. Chen · 2018
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Do better imagenet models transfer better?
S. Kornblith, J. Shlens, and Q. V. Le · 2018
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Exploring the limits of weakly supervised pretraining
D. K. Mahajan, R. B. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten · 2018
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Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2018
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Deep layer aggregation
F. Yu, D. Wang, and T. Darrell · 2018
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Taskonomy: Disentangling task transfer learning
A. R. Zamir, A. Sax, W. B. Shen, L. J. Guibas, J. Malik, and S. Savarese · 2018
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