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MultiGrain is a network architecture producing compact vector representations that are suited both for image classification and particular object retrieval.
Backpropagation applied to handwritten zip code recognition
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Video google: A text retrieval approach to object matching in videos
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Dimensionality reduction by learning an invariant mapping
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Scalable recognition with a vocabulary tree
D. Nister and H. Stewenius · 2006
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Hamming embedding and weak geometric consistency for large scale image search
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Efficient match kernel between sets of features for visual recognition
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Integral channel features
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Evaluation of gist descriptors for web-scale image search
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Better matching with fewer features: The selection of useful features in large database recognition problems
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A theoretical analysis of feature pooling in visual recognition
Y.-L. Boureau, J. Ponce, and Y. LeCun · 2010
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Negative evidences and co-occurences in image retrieval: The benefit of pca and whitening
H. Jégou and O. Chum · 2012
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Aggregating local image descriptors into compact codes
H. Jégou, F. Perronnin, M. Douze, J. Sánchez, P. Perez, and C. Schmid · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Some improvements on deep convolutional neural network based image classification
A. G. Howard · 2013
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Neural codes for image retrieval
A. Babenko, A. Slesarev, A. Chigorin, and V. Lempitsky · 2014
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Multi-scale orderless pooling of deep convolutional activation features
Y. Gong, L. Wang, R. Guo, and S. Lazebnik · 2014
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Cnn features off-the-shelf: An astounding baseline for recognition
A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson · 2014
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Visual query expansion with or without geometry: Refining local descriptors by feature aggregation
G. Tolias and H. Jégou · 2014
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S. Han, H. Mao, and W. J. Dally · 2015
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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
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Ubernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory
I. Kokkinos · 2017
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Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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Sampling matters in deep embedding learning
C.-Y. Wu, R. Manmatha, A. J. Smola, and P. Krähenbühl · 2017
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Aggregated residual transformations for deep neural networks
S. Xie, R. B. Girshick, P. Dollár, Z. Tu, and K. He · 2017
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Autoaugment: Learning augmentation policies from data
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Particular object retrieval with integral max-pooling of cnn activations
G. Tolias, R. Sicre, and H. Jégou · 2015
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Reducing overfitting in deep networks by decorrelating representations
M. Cogswell, F. Ahmed, R. B. Girshick, C. L. Zitnick, and D. Batra · 2016
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Deep image retrieval: Learning global representations for image search
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Squeeze-and-excitation networks
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Gpipe: Efficient training of giant neural networks using pipeline parallelism
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Exploring the limits of weakly supervised pretraining
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Fine-tuning CNN image retrieval with no human annotation
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Efficient parametrization of multi-domain deep neural networks
S.-A. Rebuffi, H. Bilen, and A. Vedaldi · 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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mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2018
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Progressive neural architecture search
C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy · 2018
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Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2018
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Augment your batch: better training with larger batches
E. Hoffer, T. Ben-Nun, I. Hubara, N. Giladi, T. Hoefler, and D. Soudry · 2019
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