Fetching the paper…
Reading the bibliography…
We develop a set of methods to improve on the results of self-supervised learning using context.
Margaret thatcher: a new illusion
P. Thompson · 1980
Earlier work this paper cites.
The First Stages of Processing Color and Luminance: Where and What
M. Livingstone · 2002
Earlier work this paper cites.
Best practices for convolutional neural networks applied to visual document analysis
P. Simard, D. Steinkraus, and J. Platt · 2003
Earlier work this paper cites.
Cornell Lab of Ornithology: Bird Scope
Four keys to identifying birds · 2009
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.
Caltech-UCSD Birds 200
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona · 2010
Earlier work this paper cites.
High-performance neural networks for visual object classification
D. C. Cireşan, U. Meier, J. Masci, L. M. Gambardella, and J. Schmidhuber · 2011
Earlier work this paper cites.
Multi-column deep neural networks for image classification
D. Cireşan, U. Meier, and J. Schmidhuber · 2012
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2013
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.
Very deep convolutional networks for large-scaleimage recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Learning deep features for scene recognition using places database
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva · 2014
Earlier work this paper cites.
Learning to see by moving
P. Agrawal, J. Carreira, and J. Malik · 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.
Discriminative unsupervised feature learning with exemplar convolutional neural networks
A. Dosovitskiy, P. Fischer, J. T. Springenberg, M. Riedmiller, and T. Brox · 2015
Earlier work this paper cites.
Fast r-cnn
R. Girshick · 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.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Learning image representation tied to ego-motion
D. Jayaraman and K. Grauman · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 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.
Context encoders: Feature learning by inpainting
D. Pathak, P. Krähenbühl, J. Donahue, T. Darrell, and A. Efros · 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.
Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
Later among the works it cites.
Unsupervised learning by predicting noise
P. Bojanowski and A. Joulin · 2017
Closest in time.
Multi-task self-supervised visual learning
C. Doersch and A. Zisserman · 2017
Closest in time.
Adversarial feature learning
J. Donahue, P. Krähenbühl, and T. Darrell · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Unsupervised learning of visual representations using videos
X. Wang and A. Gupta · 2015
Cited alongside, same era.
A large-scale car dataset for fine-grained categorization and verification
L. Yang, P. Luo, C. C. Loy, and X. Tang · 2015
Cited alongside, same era.
Data-dependent initializations of convolutional neural networks
P. Krähenbühl, C. Doersch, J. Donahue, and T. Darrell · 2016
Cited alongside, same era.
Unsupervised visual representation learning by graph-based consistent constraints supplementary material
D. Li, W.-C. Hung, J.-B. Huang, S. Wang, N. Ahuja, and M.-H. Yang · 2016
Cited alongside, same era.
Shuffle and learn: unsupervised learning using temporal order verification
I. Misra, C. L. Zitnick, and M. Hebert · 2016
Cited alongside, same era.
A large contextual dataset for classification, detection and counting of cars with deep learning
T. N. Mundhenk, G. Konjevod, W. A. Sakla, and K. Boakye · 2016
Cited alongside, same era.
Self-supervised learning of visual features through embedding images into text topic spaces
L. Gomez, Y. Patel, M. Rusiñol, D. Karatzas, and C. V. Jawahar · 2017
Closest in time.
Densely connected convolutional networks
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten · 2017
Closest in time.
Colorization as a proxy task for visual understanding
G. Larsson, M. Maire, and G. Shakhnarovich · 2017
Closest in time.
Unsupervised representation learning by sorting sequences
H.-Y. Lee, J.-B. Huang, M. Singh, and M.-H. Yang · 2017
Closest in time.
Representation learning by learning to count
M. Noroozi, H. Pirsiavash, and P. Favaro · 2017
Closest in time.
Learning features by watching objects move
D. Pathak, R. Girshick, P. Dollár, T. Darrell, and B. Hariharan · 2017
Closest in time.
Transitive invariance for self-supervised visual representation learning
X. Wang, K. He, and A. Gupta · 2017
Closest in time.
Split-brain autoencoders: Unsupervised learning by cross-channel prediction
R. Zhang, P. Isola, and A. A. Efros · 2017
Closest in time.
Learning image representation by completing damaged jigsaw puzzles
D. Kim, D. Cho, D. Yoo, and I. S. Kweon · 2018
Closest in time.