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Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer.
On the limited memory bfgs method for large scale optimization
Dong C Liu and Jorge Nocedal · 1989
Earlier work this paper cites.
Object recognition from local scale-invariant features
David G Lowe · 1999
Earlier work this paper cites.
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2004
Earlier work this paper cites.
Testing dependent correlations with nonoverlapping variables: A monte carlo simulation
N. Clayton Silver, James B. Hittner, and Kim May · 2004
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Histograms of oriented gradients for human detection
Navneet Dalal and Bill Triggs · 2005
Earlier work this paper cites.
Speeded-up robust features (SURF)
Herbert Bay, Andreas Ess, Tinne Tuytelaars, and Luc Van Gool · 2008
Earlier work this paper cites.
Liblinear: A library for large linear classification
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin · 2008
Earlier work this paper cites.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Confidence intervals from normalized data: A correction to cousineau (2005)
Richard D. Morey · 2008
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
Earlier work this paper cites.
Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba · 2010
Earlier work this paper cites.
Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
Earlier work this paper cites.
Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 2011
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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Collecting a large-scale dataset of fine-grained cars
Jonathan Krause, Jia Deng, Michael Stark, and Li Fei-Fei · 2013
Earlier work this paper cites.
Fine-grained visual classification of aircraft
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi · 2013
Earlier work this paper cites.
Analyzing the performance of multilayer neural networks for object recognition
Pulkit Agrawal, Ross B. Girshick, and Jitendra Malik · 2014
Earlier work this paper cites.
Birdsnap: Large-scale fine-grained visual categorization of birds
Thomas Berg, Jiongxin Liu, Seung Woo Lee, Michelle L Alexander, David W Jacobs, and Peter N Belhumeur · 2014
Earlier work this paper cites.
Food-101 — mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
Earlier work this paper cites.
Return of the devil in the details: delving deep into convolutional nets
Ken Chatfield, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
Earlier work this paper cites.
Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Spatial pyramid pooling in deep convolutional networks for visual recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2014
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CNN features off-the-shelf: an astounding baseline for recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Deep filter banks for texture recognition and segmentation
Mircea Cimpoi, Subhransu Maji, and Andrea Vedaldi · 2015
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cocor: A comprehensive solution for the statistical comparison of correlations
Birk Diedenhofen and Jochen Musch · 2015
Cited alongside, same era.
Batch normalization: accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
Cited alongside, same era.
Deeply-supervised nets
Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, and Zhuowen Tu · 2015
Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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MobileNets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Speed/accuracy trade-offs for modern convolutional object detectors
Jonathan Huang, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, and Kevin Murphy · 2017
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Dynamic computational time for visual attention
Zhichao Li, Yi Yang, Xiao Liu, Feng Zhou, Shilei Wen, and Wei Xu · 2017
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Cited alongside, same era.
Bilinear CNN models for fine-grained visual recognition
Tsung-Yu Lin, Aruni RoyChowdhury, and Subhransu Maji · 2015
Cited alongside, same era.
Faster R-CNN: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Cited alongside, same era.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Cited alongside, same era.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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.
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Locally-transferred fisher vectors for texture classification
Yang Song, Fan Zhang, Qing Li, Heng Huang, Lauren J O’Donnell, and Weidong Cai · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
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L2 regularization versus batch and weight normalization
Twan van Laarhoven · 2017
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2018
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Senteval: An evaluation toolkit for universal sentence representations
Alexis Conneau and Douwe Kiela · 2018
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Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2018
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Large scale fine-grained categorization and domain-specific transfer learning
Yin Cui, Yang Song, Chen Sun, Andrew Howard, and Serge Belongie · 2018
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Domain adaption in one-shot learning
Nanqing Dong and Eric P Xing · 2018
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Exploring the limits of weakly supervised pretraining
Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens van der Maaten · 2018
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A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2018
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Comparison of deep transfer learning strategies for digital pathology
Romain Mormont, Pierre Geurts, and Raphaël Marée · 2018
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Realistic evaluation of semi-supervised learning algorithms
Augustus Odena, Avital Oliver, Colin Raffel, Ekin Dogus Cubuk, and Ian Goodfellow · 2018
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Object-part attention model for fine-grained image classification
Yuxin Peng, Xiangteng He, and Junjie Zhao · 2018
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MobileNetV2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Adapted deep embeddings: A synthesis of methods for k-shot inductive transfer learning
Tyler Scott, Karl Ridgeway, and Michael C Mozer · 2018
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Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization
Xiu-Shen Wei, Chen-Wei Xie, Jianxin Wu, and Chunhua Shen · 2018
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Deep layer aggregation
Fisher Yu, Dequan Wang, Evan Shelhamer, and Trevor Darrell · 2018
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Taskonomy: Disentangling task transfer learning
Amir R Zamir, Alexander Sax, William Shen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese · 2018
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
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A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Three mechanisms of weight decay regularization
Guodong Zhang, Chaoqi Wang, Bowen Xu, and Roger Grosse · 2019
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