Fetching the paper…
Reading the bibliography…
While deep learning has led to significant advances in visual recognition over the past few years, such advances often require a lot of annotated data.
L. Fei-Fei, R. Fergus, and P. Perona, “ Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories.,” in
2004
Earlier work this paper cites.
G. Griffin, A. Holub, and P. Perona, “Caltech-256 object category dataset,” 2007
2007
Earlier work this paper cites.
J. Yang, R. Yan, and A. G. Hauptmann, “Cross-domain Video Concept Detection using Adaptive SVMs,” in
2007
Earlier work this paper cites.
L. Duan, I. W. Tsang, D. Xu, and T.-S. Chua, “Domain adaptation from multiple sources via auxiliary classifiers,” in
2009
Earlier work this paper cites.
K. Saenko, B. Kulis, M. Fritz, and T. Darrell, “Adapting visual category models to new domains,”
2010
Earlier work this paper cites.
R. Gopalan, R. Li, and R. Chellappa, “Domain adaptation for object recognition: An unsupervised approach,” in
2011
Earlier work this paper cites.
S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang, “Domain adaptation via transfer component analysis,”
2011
Earlier work this paper cites.
X. Glorot, A. Bordes, and Y. Bengio, “Domain adaptation for large-scale sentiment classification: A deep learning approach,” in
2011
Earlier work this paper cites.
M. Eitz, J. Hays, and M. Alexa, “How do humans sketch objects?,”
2012
Earlier work this paper cites.
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, “A kernel two-sample test,”
2012
Earlier work this paper cites.
B. Gong, Y. Shi, F. Sha, and K. Grauman, “Geodesic flow kernel for unsupervised domain adaptation,” in
2012
Earlier work this paper cites.
B. Gong, Y. Shi, F. Sha, and K. Grauman, “Geodesic flow kernel for unsupervised domain adaptation,” in
2012
Earlier work this paper cites.
I. Sutskever, J. Martens, G. Dahl, and G. Hinton, “On the importance of initialization and momentum in deep learning,” in
2013
Earlier work this paper cites.
Y. Xiang, R. Mottaghi, and S. Savarese, “Beyond pascal: A benchmark for 3d object detection in the wild,” in
2014
Cited alongside, same era.
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in
2014
Cited alongside, same era.
2014
Cited alongside, same era.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” in
2015
Cited alongside, same era.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in
2015
Cited alongside, same era.
Y. Aytar, L. Castrejon, C. Vondrick, H. Pirsiavash, and A. Torralba, “Cross-modal scene networks,”
2016
Later among the works it cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
2016
Later among the works it cites.
X. Dai, B. Singh, G. Zhang, L. S. Davis, and Y. Q. Chen, “Temporal Context Network for Activity Localization in Videos,” in
2017
Closest in time.
R. Clark, S. Wang, H. Wen, A. Markham, and N. Trigoni, “VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem,” in
2017
Closest in time.
P. Häusser, A. Mordvintsev, and D. Cremers, “Learning by Association - A versatile semi-supervised training method for neural networks,” in
2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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, “ImageNet Large Scale Visual Recognition Challenge,”
2015
Cited alongside, same era.
2015
Cited alongside, same era.
Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation,” in
2015
Cited alongside, same era.
M. Long, Y. Cao, J. Wang, and M. Jordan, “Learning transferable features with deep adaptation networks,” in
2015
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in
2015
Cited alongside, same era.
C. B. Choy, J. Gwak, S. Savarese, and M. Chandraker, “Universal Correspondence Network,” in
2016
Cited alongside, same era.
J. Zbontar and Y. LeCun, “Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches,”
2016
Cited alongside, same era.
M. Long, H. Zhu, J. Wang, and M. I. Jordan, “Deep transfer learning with joint adaptation networks,” in
2017
Closest in time.
K. Sohn, S. Liu, G. Zhong, X. Yu, M. Yang, and M. Chandraker, “Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos,” in
2017
Closest in time.
A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, and R. Webb, “Learning from Simulated and Unsupervised Images through Adversarial Training,” in
2017
Closest in time.
P. Isola, J. Zhu, T. Zhou, and A. A. Efros, “Image-to-Image Translation with Conditional Adversarial Networks,” in
2017
Closest in time.
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan, “Unsupervised pixel-level domain adaptation with generative adversarial networks,” 2017
2017
Closest in time.
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in
2017
Closest in time.