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In human learning, it is common to use multiple sources of information jointly.
Multitask learning
R. Caruana · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories
R. F. L. Fei-Fei and P. Perona · 2004
Earlier work this paper cites.
The Princeton shape benchmark
P. Shilane, P. Min, M. Kazhdan, and T. Funkhouser · 2004
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
G. E. Hinton and R. R. Salakhutdinov · 2006
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Adapting visual category models to new domains
K. Saenko, B. Kulis, M. Fritz, and T. Darrell · 2010
Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol · 2010
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.
Indoor segmentation and support inference from rgbd images
P. K. Nathan Silberman, Derek Hoiem and R. Fergus · 2012
Earlier work this paper cites.
Representation learning: A review and new perspectives
Y. Bengio, A. Courville, and P. Vincent · 2013
Earlier work this paper cites.
Seeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models
M. Aubry, D. Maturana, A. Efros, B. Russell, and J. Sivic · 2014
Earlier work this paper cites.
Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
Earlier work this paper cites.
R-cnns for pose estimation and action detection
G. Gkioxari, B. Hariharan, R. Girshick, and J. Malik · 2014
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Discriminatively trained dense surface normal estimation
L. Ladicky, B. Zeisl, and M. Pollefeys · 2014
Earlier work this paper cites.
Microsoft COCO: common objects in context
T. Lin, M. Maire, S. J. Belongie, L. D. Bourdev, R. B. Girshick, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Estimating image depth using shape collections
H. Su, Q. Huang, N. J. Mitra, Y. Li, and L. Guibas · 2014
Earlier work this paper cites.
Facial landmark detection by deep multi-task learning
Z. Zhang, P. Luo, C. C. Loy, and X. Tang · 2014
Earlier work this paper cites.
Learning to see by moving
P. Agrawal, J. Carreira, and J. Malik · 2015
Earlier work this paper cites.
Shapenet: An information-rich 3d model repository
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu · 2015
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
Earlier work this paper cites.
Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
D. Eigen and R. Fergus · 2015
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Y. Ganin and V. S. Lempitsky · 2015
Earlier work this paper cites.
Fast R-CNN
R. B. Girshick · 2015
Earlier work this paper cites.
Single-view reconstruction via joint analysis of image and shape collections
Q. Huang, H. Wang, and V. Koltun · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Learning image representations tied to egomotion
D. Jayaraman and K. Grauman · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
S. R. Kaiming He, Xiangyu Zhang and J. Sun · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Cited alongside, same era.
Learning deep object detectors from 3d models
X. Peng, B. Sun, K. Ali, and K. Saenko · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
Playing for data: Ground truth from computer games
S. R. Richter, V. Vineet, S. Roth, and V. Koltun · 2016
Later among the works it cites.
The SYNTHIA Dataset: A large collection of synthetic images for semantic segmentation of urban scenes
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. Lopez · 2016
Later among the works it cites.
Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
Later among the works it cites.
Unrealstereo: A synthetic dataset for analyzing stereo vision
Y. Zhang, W. Qiu, Q. Chen, X. Hu, and A. L. Yuille · 2016
Later among the works it cites.
Places: An image database for deep scene understanding
B. Zhou, A. Khosla, A. Lapedriza, A. Torralba, and A. Oliva · 2016
Later among the works it cites.
Look, listen and learn
R. Arandjelovic and A. Zisserman · 2017
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Unsupervised learning of visual representations using videos
X. Wang and A. Gupta · 2015
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Galileo: Perceiving physical object properties by integrating a physics engine with deep learning
J. Wu, I. Yildirim, J. J. Lim, W. T. Freeman, and J. B. Tenenbaum · 2015
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Holistically-nested edge detection
S. Xie and Z. Tu · 2015
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Multi-scale context aggregation by dilated convolutions
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Play and learn: Using video games to train computer vision models
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Image-to-image translation with conditional adversarial networks
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Semantic scene completion from a single depth image
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Transitive invariance for self-supervised visual representation learning
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
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Physically-based rendering for indoor scene understanding using convolutional neural networks
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Unpaired image-to-image translation using cycle-consistent adversarial networks
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