Fetching the paper…
Reading the bibliography…
Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives.
Some methods for classification and analysis of multivariate observations
J. MacQueen et al · 1967
Earlier work this paper cites.
Is learning the n-th thing any easier than learning the first?
S. Thrun · 1996
Earlier work this paper cites.
Multitask learning
R. Caruana · 1998
Earlier work this paper cites.
Optics: ordering points to identify the clustering structure
M. Ankerst, M. M. Breunig, H.-P. Kriegel, and J. Sander · 1999
Earlier work this paper cites.
A model of inductive bias learning
J. Baxter et al · 2000
Earlier work this paper cites.
Mean shift: A robust approach toward feature space analysis
D. Comaniciu and P. Meer · 2002
Earlier work this paper cites.
Accurate and efficient stereo processing by semi-global matching and mutual information
H. Hirschmuller · 2005
Earlier work this paper cites.
A unified architecture for natural language processing: Deep neural networks with multitask learning
R. Collobert and J. Weston · 2008
Earlier work this paper cites.
Stereo processing by semiglobal matching and mutual information
H. Hirschmuller · 2008
Earlier work this paper cites.
Robust object detection with interleaved categorization and segmentation
B. Leibe, A. Leonardis, and B. Schiele · 2008
Earlier work this paper cites.
Multimodal deep learning
J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng · 2011
Earlier work this paper cites.
Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers
J.-T. Huang, J. Li, D. Yu, L. Deng, and Y. Gong · 2013
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Earlier work this paper cites.
Hypercolumns for object segmentation and fine-grained localization
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2014
Earlier work this paper cites.
Learning and transferring mid-level image representations using convolutional neural networks
M. Oquab, L. Bottou, I. Laptev, and J. Sivic · 2014
Earlier work this paper cites.
Overfeat: Integrated recognition, localization and detection using convolutional networks
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun · 2014
Cited alongside, same era.
Learning to see by moving
P. Agrawal, J. Carreira, and J. Malik · 2015
Cited alongside, same era.
Semantic image segmentation with deep convolutional nets and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2015
Cited alongside, same era.
Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
D. Eigen and R. Fergus · 2015
Cited alongside, same era.
Convolutional networks for real-time 6-dof camera relocalization
A. Kendall, M. Grimes, and R. Cipolla · 2015
Cited alongside, same era.
Proposal-free network for instance-level object segmentation
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Later among the works it cites.
I. Kokkinos · 2016
Later among the works it cites.
Understand scene categories by objects: A semantic regularized scene classifier using convolutional neural networks
Y. Liao, S. Kodagoda, Y. Wang, L. Shi, and Y. Liu · 2016
Later among the works it cites.
Cross-stitch networks for multi-task learning
I. Misra, A. Shrivastava, A. Gupta, and M. Hebert · 2016
Later among the works it cites.
Multinet: Real-time joint semantic reasoning for autonomous driving
M. Teichmann, M. Weber, M. Zoellner, R. Cipolla, and R. Urtasun · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
X. Liang, Y. Wei, X. Shen, J. Yang, L. Lin, and S. Yan · 2015
Cited alongside, same era.
Efficient piecewise training of deep structured models for semantic segmentation
G. Lin, C. Shen, I. Reid, et al · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Cited alongside, same era.
Learning to segment object candidates
P. O. Pinheiro, R. Collobert, and P. Dollar · 2015
Cited alongside, same era.
Conditional random fields as recurrent neural networks
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. Torr · 2015
Cited alongside, same era.
Deep watershed transform for instance segmentation
M. Bai and R. Urtasun · 2016
Cited alongside, same era.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2016
Cited alongside, same era.
J. Uhrig, M. Cordts, U. Franke, and T. Brox · 2016
Later among the works it cites.
Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
Later among the works it cites.
Wide residual networks
S. Zagoruyko and N. Komodakis · 2016
Later among the works it cites.
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2016
Later among the works it cites.
Segnet: A deep convolutional encoder-decoder architecture for scene segmentation
V. Badrinarayanan, A. Kendall, and R. Cipolla · 2017
Closest in time.
In-place activated batchnorm for memory-optimized training of dnns
S. R. Bulò, L. Porzi, and P. Kontschieder · 2017
Closest in time.
Rethinking atrous convolution for semantic image segmentation
L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam · 2017
Closest in time.
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Closest in time.
What uncertainties do we need in bayesian deep learning for computer vision?
A. Kendall and Y. Gal · 2017
Closest in time.
Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, et al · 2017
Closest in time.