Fetching the paper…
Reading the bibliography…
Domain Adaptation (DA) approaches achieved significant improvements in a wide range of machine learning and computer vision tasks (i.e., classification, detection, and segmentation).
Learning representations by back-propagating errors
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1986
Earlier work this paper cites.
Integrating structured biological data by kernel maximum mean discrepancy
K. M. Borgwardt, A. Gretton, M. J. Rasch, H.-P. Kriegel, B. Schölkopf, and A. J. Smola · 2006
Earlier work this paper cites.
Direct importance estimation with model selection and its application to covariate shift adaptation
M. Sugiyama, S. Nakajima, H. Kashima, P. V. Buenau, and M. Kawanabe · 2008
Earlier work this paper cites.
A theory of learning from different domains
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan · 2010
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
Earlier work this paper cites.
Domain adaptation via transfer component analysis
S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang · 2010
Earlier work this paper cites.
Domain adaptation for object recognition: An unsupervised approach
R. Gopalan, R. Li, and R. Chellappa · 2011
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.
Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation
B. Gong, K. Grauman, and F. Sha · 2013
Earlier work this paper cites.
Transfer feature learning with joint distribution adaptation
M. Long, J. Wang, G. Ding, J. Sun, and P. S. Yu · 2013
Earlier work this paper cites.
Domain adaptation under target and conditional shift
K. Zhang, B. Schölkopf, K. Muandet, and Z. Wang · 2013
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 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.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
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, et al · 2015
Cited alongside, same era.
VoxNet: A 3D convolutional neural network for real-time object recognition
D. Maturana and S. Scherer · 2015
Cited alongside, same era.
Multi-view convolutional neural networks for 3D shape recognition
H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller · 2015
Cited alongside, same era.
3D shapenets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
Cited alongside, same era.
PointCNN: Convolution on X-transformed points
Y. Li, R. Bu, M. Sun, W. Wu, X. Di, and B. Chen · 2018
Later among the works it cites.
Maximum classifier discrepancy for unsupervised domain adaptation
K. Saito, K. Watanabe, Y. Ushiku, and T. Harada · 2018
Later among the works it cites.
PVNet: A joint convolutional network of point cloud and multi-view for 3d shape recognition
H. You, Y. Feng, R. Ji, and Y. Gao · 2018
Later among the works it cites.
Image super-resolution using very deep residual channel attention networks
Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu · 2018
Later among the works it cites.
Unsupervised domain adaptation for 3d keypoint estimation via view consistency
X. Zhou, A. Karpur, C. Gan, L. Luo, and Q. Huang · 2018
Later among the works it cites.
VoxelNet: End-to-end learning for point cloud based 3d object detection
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Deep coral: Correlation alignment for deep domain adaptation
B. Sun and K. Saenko · 2016
Cited alongside, same era.
Joint distribution optimal transportation for domain adaptation
N. Courty, R. Flamary, A. Habrard, and A. Rakotomamonjy · 2017
Cited alongside, same era.
ScanNet: Richly-annotated 3D reconstructions of indoor scenes
A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner · 2017
Cited alongside, same era.
Deformable convolutional networks
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei · 2017
Cited alongside, same era.
PointNet: Deep learning on point sets for 3D classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
Cited alongside, same era.
PointNet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
Cited alongside, same era.
Y. Zhou and O. Tuzel · 2018
Later among the works it cites.
Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Y. Zou, Z. Yu, B. Vijaya Kumar, and J. Wang · 2018
Later among the works it cites.
Semantic-transferable weakly-supervised endoscopic lesions segmentation
J. Dong, Y. Cong, G. Sun, and D. Hou · 2019
Closest in time.
MeshNet: mesh neural network for 3D shape representation
Y. Feng, Y. Feng, H. You, X. Zhao, and Y. Gao · 2019
Closest in time.
Generatively inferential co-training for unsupervised domain adaptation
C. Qin, L. Wang, Y. Zhang, and Y. Fu · 2019
Closest in time.
Domain adaptation for vehicle detection from bird’s eye view LiDAR point cloud data
K. Saleh, A. Abobakr, M. Attia, J. Iskander, D. Nahavandi, and M. Hossny · 2019
Closest in time.
Low-rank transfer human motion segmentation
L. Wang, Z. Ding, and Y. Fu · 2019
Closest in time.
SqueezeSegV2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud
B. Wu, X. Zhou, S. Zhao, X. Yue, and K. Keutzer · 2019
Closest in time.
PVRNet: Point-view relation neural network for 3D shape recognition
H. You, Y. Feng, X. Zhao, C. Zou, R. Ji, and Y. Gao · 2019
Closest in time.