Fetching the paper…
Reading the bibliography…
Although deep networks have significantly increased the performance of visual recognition methods, it is still challenging to achieve the robustness across visual domains that is necessary for real-world applications.
Multitask learning
R. Caruana · 1997
Earlier work this paper cites.
A new learning paradigm: Learning using privileged information
V. Vapnik and A. Vashist · 2009
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. Vaughan · 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.
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
B. Kulis, K. Saenko, and T. Darrell · 2011
Earlier work this paper cites.
Discovering latent domains for multisource domain adaptation
J. Hoffman, B. Kulis, T. Darrell, and K. Saenko · 2012
Earlier work this paper cites.
Undoing the damage of dataset bias
A. Khosla, T. Zhou, T. Malisiewicz, A. Efros, and A. Torralba · 2012
Earlier work this paper cites.
Semi-supervised domain adaptation with instance constraints
J. Donahue, J. Hoffman, E. Rodner, K. Saenko, and T. Darrell · 2013
Earlier work this paper cites.
Domain generalization via invariant feature representation
K. Muandet, D. Balduzzi, and B. Schölkopf · 2013
Earlier work this paper cites.
Discriminative unsupervised feature learning with convolutional neural networks
A. Dosovitskiy, J. T. Springenberg, M. Riedmiller, and T. Brox · 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.
Simultaneous detection and segmentation
B. Hariharan, P. A. Arbeláez, R. B. 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.
How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2015
Earlier work this paper cites.
Domain generalization for object recognition with multi-task autoencoders
M. Ghifary, W. B. Kleijn, M. Zhang, and D. Balduzzi · 2015
Earlier work this paper cites.
A century of portraits: A visual historical record of american high school yearbooks
S. Ginosar, K. Rakelly, S. Sachs, B. Yin, and A. A. Efros · 2015
Earlier work this paper cites.
Fast r-cnn
R. Girshick · 2015
Earlier work this paper cites.
Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. I. Jordan · 2015
Earlier work this paper cites.
Unsupervised learning of visual representations using videos
X. Wang and A. Gupta · 2015
Earlier work this paper cites.
A large-scale car dataset for fine-grained categorization and verification
L. Yang, P. Luo, C. Change Loy, and X. Tang · 2015
Earlier work this paper cites.
Semi-supervised domain adaptation with subspace learning for visual recognition
T. Yao, Y. Pan, C.-W. Ngo, H. Li, and T. Mei · 2015
Earlier work this paper cites.
Domain Separation Networks
K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan · 2016
Earlier work this paper cites.
Domain-adversarial training of neural networks
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky · 2016
Earlier work this paper cites.
Cross-stitch networks for multi-task learning
I. Misra, A. Shrivastava, A. Gupta, and M. Hebert · 2016
Earlier work this paper cites.
Unsupervised learning of visual representations by solving jigsaw puzzles
M. Noroozi and P. Favaro · 2016
Earlier work this paper cites.
Context encoders: Feature learning by inpainting
D. Pathak, P. Krähenbühl, J. Donahue, T. Darrell, and A. Efros · 2016
Earlier work this paper cites.
Deep coral: Correlation alignment for deep domain adaptation
B. Sun and K. Saenko · 2016
Earlier work this paper cites.
Multivariate regression on the grassmannian for predicting novel domains
Y. Yang and T. M. Hospedales · 2016
Cited alongside, same era.
Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
Cited alongside, same era.
Autodial: Automatic domain alignment layers
F. M. Carlucci, L. Porzi, B. Caputo, E. Ricci, and S. Rota Bulò · 2017
Cited alongside, same era.
Domain Adaptation in Computer Vision Applications
G. Csurka, editor · 2017
Cited alongside, same era.
Deep domain generalization with structured low-rank constraint
Z. Ding and Y. Fu · 2017
Cited alongside, same era.
Multi-task self-supervised visual learning
C. Doersch and A. Zisserman · 2017
Cited alongside, same era.
Domain generalization with adversarial feature learning
H. Li, S. Jialin Pan, S. Wang, and A. C. Kot · 2018
Later among the works it cites.
Explicit Inductive Bias for Transfer Learning with Convolutional Networks
X. Li, Y. Grandvalet, and F. Davoine · 2018
Later among the works it cites.
Deep domain generalization via conditional invariant adversarial networks
Y. Li, X. Tian, M. Gong, Y. Liu, T. Liu, K. Zhang, and D. Tao · 2018
Later among the works it cites.
Auxiliary tasks in multi-task learning
L. Liebel and M. Körner · 2018
Later among the works it cites.
Robust place categorization with deep domain generalization
M. Mancini, S. R. Bulo, B. Caputo, and E. Ricci · 2018
Later among the works it cites.
Boosting domain adaptation by discovering latent domains
M. Mancini, L. Porzi, S. Rota Bulò, B. Caputo, and E. Ricci · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
C. Doersch and A. Zisserman · 2017
Cited alongside, same era.
Ubernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory
I. Kokkinos · 2017
Cited alongside, same era.
Deeper, broader and artier domain generalization
D. Li, Y. Yang, Y. Z. Song, and T. M. Hospedales · 2017
Cited alongside, same era.
Deep transfer learning with joint adaptation networks
M. Long, H. Zhu, J. Wang, and M. I. Jordan · 2017
Cited alongside, same era.
Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification
Y. Lu, A. Kumar, S. Zhai, Y. Cheng, T. Javidi, and R. S. Feris · 2017
Cited alongside, same era.
Unified deep supervised domain adaptation and generalization
S. Motiian, M. Piccirilli, D. A. Adjeroh, and G. Doretto · 2017
Cited alongside, same era.
Revisiting multi-task learning with rock: a deep residual auxiliary block for visual detection
T. Mordan, N. THOME, G. Henaff, and M. Cord · 2018
Later among the works it cites.
Boosting self-supervised learning via knowledge transfer
M. Noroozi, A. Vinjimoor, P. Favaro, and H. Pirsiavash · 2018
Later among the works it cites.
Audio-visual scene analysis with self-supervised multisensory features
A. Owens and A. A. Efros · 2018
Later among the works it cites.
Cross-domain self-supervised multi-task feature learning using synthetic imagery
Z. Ren and Y. J. Lee · 2018
Later among the works it cites.
From source to target and back: symmetric bi-directional adaptive gan
P. Russo, F. M. Carlucci, T. Tommasi, and B. Caputo · 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.
Open set domain adaptation by backpropagation
K. Saito, S. Yamamoto, Y. Ushiku, and T. Harada · 2018
Later among the works it cites.
Generate to adapt: Aligning domains using generative adversarial networks
S. Sankaranarayanan, Y. Balaji, C. D. Castillo, and R. Chellappa · 2018
Later among the works it cites.
Time-contrastive networks: Self-supervised learning from video
P. Sermanet, C. Lynch, Y. Chebotar, J. Hsu, E. Jang, S. Schaal, and S. Levine · 2018
Later among the works it cites.
Generalizing across domains via cross-gradient training
S. Shankar, V. Piratla, S. Chakrabarti, S. Chaudhuri, P. Jyothi, and S. Sarawagi · 2018
Later among the works it cites.
Generalizing to unseen domains via adversarial data augmentation
R. Volpi, H. Namkoong, O. Sener, J. Duchi, V. Murino, and S. Savarese · 2018
Later among the works it cites.
Deep cocktail network: Multi-source unsupervised domain adaptation with category shift
R. Xu, Z. Chen, W. Zuo, J. Yan, and L. Lin · 2018
Later among the works it cites.
Semi-supervised optimal transport for heterogeneous domain adaptation
Y. Yan, W. Li, H. Wu, H. Min, M. Tan, and Q. Wu · 2018
Later among the works it cites.
Domain generalization by solving jigsaw puzzles
F. M. Carlucci, A. D’Innocente, S. Bucci, B. Caputo, and T. Tommasi · 2019
Closest in time.
Dlow: Domain flow for adaptation and generalization
R. Gong, W. Li, Y. Chen, and L. V. Gool · 2019
Closest in time.
Spottune: Transfer learning through adaptive fine-tuning
Y. Guo, H. Shi, A. Kumar, K. Grauman, T. Rosing, and R. Feris · 2019
Closest in time.
Self-supervised visual feature learning with deep neural networks: A survey
L. Jing and Y. Tian · 2019
Closest in time.
Making sense of vision and touch: Self-supervised learning of multimodal representations for contact-rich tasks
M. A. Lee, Y. Zhu*, K. Srinivasan, P. Shah, S. Savarese, L. Fei-Fei, A. Garg, and J. Bohg · 2019
Closest in time.
Episodic training for domain generalization
D. Li, J. Zhang, Y. Yang, C. Liu, Y.-Z. Song, and T. M. Hospedales · 2019
Closest in time.
Self-supervised generalisation with meta auxiliary learning
S. Liu, A. J. Davison, and E. Johns · 2019
Closest in time.
Adagraph: Unifying predictive and continuous domain adaptation through graphs
M. Mancini, S. R. Bulo, B. Caputo, and E. Ricci · 2019
Closest in time.