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 ## Survey papers
 
-- [Domain Generalization: A Survey](https://arxiv.org/abs/2103.02503)  
-Zhou, Kaiyang, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy.  
-*arXiv preprint arXiv:2103.02503* (2021).  
+- [Domain Generalization: A Survey](https://arxiv.org/abs/2103.02503)
+Zhou, Kaiyang, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy.
+*arXiv preprint arXiv:2103.02503* (2021).
 
-- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://arxiv.org/abs/2103.03097)  
-Wang, Jindong, Cuiling Lan, Chang Liu, Yidong Ouyang, Wenjun Zeng, and Tao Qin.  
-*International Joint Conference on Artificial Intelligence* (**IJCAI**) (2021).  
+- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://arxiv.org/abs/2103.03097)
+Wang, Jindong, Cuiling Lan, Chang Liu, Yidong Ouyang, Wenjun Zeng, and Tao Qin.
+*International Joint Conference on Artificial Intelligence* (**IJCAI**) (2021).
 
 ## Research papers 2021
 
 <h3 id="ml2021">Machine learning venues</h3>
 
-- (**IB-IRM**) [Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization](https://arxiv.org/abs/2106.06607)  
-Ahuja, Kartik, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, and Irina Rish.  
-*Neural Information Processing Systems* (**NeurIPS**) 2021.  
+- (**IB-IRM**) [Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization](https://arxiv.org/abs/2106.06607)
+Ahuja, Kartik, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, and Irina Rish.
+*Neural Information Processing Systems* (**NeurIPS**) 2021.
 [[code]](https://github.com/ahujak/IB-IRM)
 
-- (**MatchDG**) [Domain Generalization using Causal Matching](http://proceedings.mlr.press/v139/mahajan21b.html)  
-Mahajan, Divyat, Shruti Tople, and Amit Sharma.  
-*International Conference of Machine Learning* (**ICML**) (2021).  
+- (**MatchDG**) [Domain Generalization using Causal Matching](http://proceedings.mlr.press/v139/mahajan21b.html)
+Mahajan, Divyat, Shruti Tople, and Amit Sharma.
+*International Conference of Machine Learning* (**ICML**) (2021).
 [[code]](https://github.com/microsoft/robustdg)
 
-- (**VBCLS**) [Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference](https://www.ijcai.org/proceedings/2021/122)  
-Liu, Xiaofeng, Bo Hu, Linghao Jin, Xu Han, Fangxu Xing, Jinsong Ouyang, Jun Lu, Georges EL Fakhri, and Jonghye Woo.  
-*International Joint Conference on Artificial Intelligence* (**IJCAI**) (2021).  
+- (**VBCLS**) [Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference](https://www.ijcai.org/proceedings/2021/122)
+Liu, Xiaofeng, Bo Hu, Linghao Jin, Xu Han, Fangxu Xing, Jinsong Ouyang, Jun Lu, Georges EL Fakhri, and Jonghye Woo.
+*International Joint Conference on Artificial Intelligence* (**IJCAI**) (2021).
 
-- (**MixStyle**) [Domain Generalization with MixStyle](https://openreview.net/forum?id=6xHJ37MVxxp)  
-Zhou, Kaiyang, Yongxin Yang, Yu Qiao, and Tao Xiang.  
-*International Conference on Learning Representations* (**ICLR**) 2021.  
+- (**MixStyle**) [Domain Generalization with MixStyle](https://openreview.net/forum?id=6xHJ37MVxxp)
+Zhou, Kaiyang, Yongxin Yang, Yu Qiao, and Tao Xiang.
+*International Conference on Learning Representations* (**ICLR**) 2021.
 [[code]](https://github.com/KaiyangZhou/mixstyle-release)
 
-- [The Risks of Invariant Risk Minimization](https://openreview.net/forum?id=BbNIbVPJ-42)  
-Rosenfeld, Elan, Pradeep Ravikumar, and Andrej Risteski.  
-*International Conference on Learning Representations* (**ICLR**) 2021.  
+- [The Risks of Invariant Risk Minimization](https://openreview.net/forum?id=BbNIbVPJ-42)
+Rosenfeld, Elan, Pradeep Ravikumar, and Andrej Risteski.
+*International Conference on Learning Representations* (**ICLR**) 2021.
 
-- (**DomainBed**) [In Search of Lost Domain Generalization](https://openreview.net/forum?id=lQdXeXDoWtI)  
-Gulrajani, Ishaan, and David Lopez-Paz.  
-*International Conference on Learning Representations* (**ICLR**) 2021.  
+- (**DomainBed**) [In Search of Lost Domain Generalization](https://openreview.net/forum?id=lQdXeXDoWtI)
+Gulrajani, Ishaan, and David Lopez-Paz.
+*International Conference on Learning Representations* (**ICLR**) 2021.
 [[code]](https://github.com/facebookresearch/DomainBed)
 
-- [Domain Generalization by Marginal Transfer Learning](https://www.jmlr.org/papers/volume22/17-679/17-679.pdf)  
-Blanchard, Gilles, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, and Clayton Scott.  
+- [Domain Generalization by Marginal Transfer Learning](https://www.jmlr.org/papers/volume22/17-679/17-679.pdf)
+Blanchard, Gilles, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, and Clayton Scott.
 *Journal of Machine Learning Research* (**JMLR**) (2021).
 
 <h3 id="cv2021">Computer vision venues</h3>
 
-- [Learning to Diversify for Single Domain Generalization](https://arxiv.org/abs/2108.11726)  
-Wang, Zijian, Yadan Luo, Ruihong Qiu, Zi Huang, and Mahsa Baktashmotlagh.  
-*Proceedings of the IEEE International Conference on Computer Vision* (**ICCV**) 2021.  
+- (**XDED**) [Cross-Domain Ensemble Distillation for Domain Generalization](https://arxiv.org/abs/2211.14058)
+Kyungmoon Lee, Sungyeon Kim, and Suha Kwak.
+*Proceedings of the European Conference on Computer Vision* (**ECCV**) 2022.
+[[code]](https://github.com/leekyungmoon/XDED)
+
+- [Learning to Diversify for Single Domain Generalization](https://arxiv.org/abs/2108.11726)
+Wang, Zijian, Yadan Luo, Ruihong Qiu, Zi Huang, and Mahsa Baktashmotlagh.
+*Proceedings of the IEEE International Conference on Computer Vision* (**ICCV**) 2021.
 [[code]](https://github.com/BUserName/Learning_to_diversify)
 
-- (**NSAE**) [Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder](https://arxiv.org/abs/2108.05028)  
-Liang, Hanwen, Qiong Zhang, Peng Dai, and Juwei Lu.  
-*Proceedings of the IEEE International Conference on Computer Vision* (**ICCV**) 2021.  
+- (**NSAE**) [Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder](https://arxiv.org/abs/2108.05028)
+Liang, Hanwen, Qiong Zhang, Peng Dai, and Juwei Lu.
+*Proceedings of the IEEE International Conference on Computer Vision* (**ICCV**) 2021.
 
-- (**Agr**) [Domain Generalization via Gradient Surgery](https://arxiv.org/abs/2108.01621)  
-Mansilla, Lucas, Rodrigo Echeveste, Diego H. Milone, and Enzo Ferrante.  
-*Proceedings of the IEEE International Conference on Computer Vision* (**ICCV**) 2021.  
+- (**Agr**) [Domain Generalization via Gradient Surgery](https://arxiv.org/abs/2108.01621)
+Mansilla, Lucas, Rodrigo Echeveste, Diego H. Milone, and Enzo Ferrante.
+*Proceedings of the IEEE International Conference on Computer Vision* (**ICCV**) 2021.
 [[code]](https://github.com/lucasmansilla/DGvGS)
 
-- (**ASR-Norm**) [Adversarially Adaptive Normalization for Single Domain Generalization](https://arxiv.org/abs/2106.01899)  
-Fan, Xinjie, Qifei Wang, Junjie Ke, Feng Yang, Boqing Gong, and Mingyuan Zhou.  
-*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.  
+- (**ASR-Norm**) [Adversarially Adaptive Normalization for Single Domain Generalization](https://arxiv.org/abs/2106.01899)
+Fan, Xinjie, Qifei Wang, Junjie Ke, Feng Yang, Boqing Gong, and Mingyuan Zhou.
+*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.
 
-- [A Fourier-based Framework for Domain Generalization](https://arxiv.org/abs/2105.11120)  
-Xu, Qinwei, Ruipeng Zhang, Ya Zhang, Yanfeng Wang, and Qi Tian.  
-*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.  
+- [A Fourier-based Framework for Domain Generalization](https://arxiv.org/abs/2105.11120)
+Xu, Qinwei, Ruipeng Zhang, Ya Zhang, Yanfeng Wang, and Qi Tian.
+*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.
 
-- (**semanticGAN**) [Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization](https://arxiv.org/abs/2104.05833)  
-Li, Daiqing, Junlin Yang, Karsten Kreis, Antonio Torralba, and Sanja Fidler.  
-*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.  
+- (**semanticGAN**) [Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization](https://arxiv.org/abs/2104.05833)
+Li, Daiqing, Junlin Yang, Karsten Kreis, Antonio Torralba, and Sanja Fidler.
+*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.
 [[code]](https://nv-tlabs.github.io/semanticGAN/)
 
-- (**RobustNet**) [RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening](https://arxiv.org/abs/2103.15597)  
-Choi, Sungha, Sanghun Jung, Huiwon Yun, Joanne Kim, Seungryong Kim, and Jaegul Choo.  
-*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.  
+- (**RobustNet**) [RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening](https://arxiv.org/abs/2103.15597)
+Choi, Sungha, Sanghun Jung, Huiwon Yun, Joanne Kim, Seungryong Kim, and Jaegul Choo.
+*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.
 [[code]](https://github.com/shachoi/RobustNet)
 
-- (**PDEN**) [Progressive Domain Expansion Network for Single Domain Generalization](https://arxiv.org/abs/2103.16050)  
-Li, Lei, Ke Gao, Juan Cao, Ziyao Huang, Yepeng Weng, Xiaoyue Mi, Zhengze Yu, and Xiaoya Li.  
-*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.  
+- (**PDEN**) [Progressive Domain Expansion Network for Single Domain Generalization](https://arxiv.org/abs/2103.16050)
+Li, Lei, Ke Gao, Juan Cao, Ziyao Huang, Yepeng Weng, Xiaoyue Mi, Zhengze Yu, and Xiaoya Li.
+*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.
 [[code]](https://github.com/lileicv/PDEN)
 
-- (**ELCFS**) [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://arxiv.org/abs/2103.06030)  
-Liu, Quande, Cheng Chen, Jing Qin, Qi Dou, and Pheng-Ann Heng.  
-*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.  
+- (**ELCFS**) [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://arxiv.org/abs/2103.06030)
+Liu, Quande, Cheng Chen, Jing Qin, Qi Dou, and Pheng-Ann Heng.
+*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.
 [[code]](https://github.com/liuquande/FedDG-ELCFS)
 
-- (**FSDR**) [FSDR: Frequency Space Domain Randomization for Domain Generalization](https://arxiv.org/abs/2103.02370)  
-Huang, Jiaxing, Dayan Guan, Aoran Xiao, and Shijian Lu.  
-*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.  
+- (**FSDR**) [FSDR: Frequency Space Domain Randomization for Domain Generalization](https://arxiv.org/abs/2103.02370)
+Huang, Jiaxing, Dayan Guan, Aoran Xiao, and Shijian Lu.
+*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.
 
-- [Domain Generalization via Inference-time Label-Preserving Target Projections](https://arxiv.org/abs/2103.01134)  
-Pandey, Prashant, Mrigank Raman, Sumanth Varambally, and Prathosh AP.  
-*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.  
+- [Domain Generalization via Inference-time Label-Preserving Target Projections](https://arxiv.org/abs/2103.01134)
+Pandey, Prashant, Mrigank Raman, Sumanth Varambally, and Prathosh AP.
+*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.
 
-- [Adaptive Methods for Real-World Domain Generalization](https://arxiv.org/abs/2103.15796)  
-Dubey, Abhimanyu, Vignesh Ramanathan, Alex Pentland, and Dhruv Mahajan.  
-*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.  
+- [Adaptive Methods for Real-World Domain Generalization](https://arxiv.org/abs/2103.15796)
+Dubey, Abhimanyu, Vignesh Ramanathan, Alex Pentland, and Dhruv Mahajan.
+*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.
 
 <h3 id="arxiv2021">arXiv</h3>
 
-- [Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments.](https://arxiv.org/abs/2106.09913)  
-Chen, Yining, Elan Rosenfeld, Mark Sellke, Tengyu Ma, and Andrej Risteski.  
-*arXiv preprint arXiv:2106.09913* (2021).  
+- [Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments.](https://arxiv.org/abs/2106.09913)
+Chen, Yining, Elan Rosenfeld, Mark Sellke, Tengyu Ma, and Andrej Risteski.
+*arXiv preprint arXiv:2106.09913* (2021).
 
 
 ## Research papers before 2021
 
 <h3 id="pathfinder">Pathfinder</h3>
 
-- [Generalizing from several related classification tasks to a new unlabeled sample](http://papers.nips.cc/paper/4312-generalizing-from-several-related-classification-tasks-to-a-new-unlabeled-sample.pdf)  
-Blanchard, Gilles, Gyemin Lee, and Clayton Scott.  
+- [Generalizing from several related classification tasks to a new unlabeled sample](http://papers.nips.cc/paper/4312-generalizing-from-several-related-classification-tasks-to-a-new-unlabeled-sample.pdf)
+Blanchard, Gilles, Gyemin Lee, and Clayton Scott.
 *Advances in neural information processing systems.* (**NIPS**) 2011.
 
 <h3 id="mlbf2021">Machine learning venues</h3>
 
 #### Neural network-based methods
 
-- [Domain Generalization via Entropy Regularization](https://proceedings.neurips.cc/paper/2020/hash/b98249b38337c5088bbc660d8f872d6a-Abstract.html)  
-Zhao, Shanshan, Mingming Gong, Tongliang Liu, Huan Fu, and Dacheng Tao.  
-*Neural Information Processing Systems* (**NeurIPS**) 2020.  
+- [Domain Generalization via Entropy Regularization](https://proceedings.neurips.cc/paper/2020/hash/b98249b38337c5088bbc660d8f872d6a-Abstract.html)
+Zhao, Shanshan, Mingming Gong, Tongliang Liu, Huan Fu, and Dacheng Tao.
+*Neural Information Processing Systems* (**NeurIPS**) 2020.
 [[code]](https://github.com/sshan-zhao/DG_via_ER)
 
-- (**LDDG**) [Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization](https://proceedings.neurips.cc/paper/2020/hash/201d7288b4c18a679e48b31c72c30ded-Abstract.html)  
-Li, Haoliang, YuFei Wang, Renjie Wan, Shiqi Wang, Tie-Qiang Li, and Alex C. Kot.  
-*Neural Information Processing Systems* (**NeurIPS**) 2020.  
+- (**LDDG**) [Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization](https://proceedings.neurips.cc/paper/2020/hash/201d7288b4c18a679e48b31c72c30ded-Abstract.html)
+Li, Haoliang, YuFei Wang, Renjie Wan, Shiqi Wang, Tie-Qiang Li, and Alex C. Kot.
+*Neural Information Processing Systems* (**NeurIPS**) 2020.
 [[code]](https://github.com/wyf0912/LDDG)
 
-- (**CSD**) [Efficient Domain Generalization via Common-Specific Low-Rank Decomposition](https://arxiv.org/abs/2003.12815)  
-Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi  
-*International Conference on Machine Learning* (**ICML**) 2020.  
+- (**CSD**) [Efficient Domain Generalization via Common-Specific Low-Rank Decomposition](https://arxiv.org/abs/2003.12815)
+Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi
+*International Conference on Machine Learning* (**ICML**) 2020.
 [[code]](https://github.com/vihari/CSD)
 
-- (**GCFN**) [Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition.](https://openreview.net/pdf?id=H1lxVyStPH)  
-Ryu, Jongbin, Gitaek Kwon, Ming-Hsuan Yang, and Jongwoo Lim.  
+- (**GCFN**) [Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition.](https://openreview.net/pdf?id=H1lxVyStPH)
+Ryu, Jongbin, Gitaek Kwon, Ming-Hsuan Yang, and Jongwoo Lim.
 *International Conference on Learning Representations* (**ICLR**) 2020.
 
-- (**MASF**) [Domain Generalization via Model-Agnostic Learning of Semantic Features.](https://arxiv.org/abs/1910.13580)  
-Qi Dou, Daniel C. Castro, Konstantinos Kamnitsas, and Ben Glocker.  
-*Advances in Neural Information Processing Systems* (**NeurIPS**) 2019.   
+- (**MASF**) [Domain Generalization via Model-Agnostic Learning of Semantic Features.](https://arxiv.org/abs/1910.13580)
+Qi Dou, Daniel C. Castro, Konstantinos Kamnitsas, and Ben Glocker.
+*Advances in Neural Information Processing Systems* (**NeurIPS**) 2019.
 [[code]](https://github.com/biomedia-mira/masf)
 
-- (**CAADA**) [Correlation-aware Adversarial Domain Adaptation and Generalization](https://www.sciencedirect.com/science/article/pii/S003132031930425X)  
-Rahman, Mohammad Mahfujur, Clinton Fookes, Mahsa Baktashmotlagh, and Sridha Sridharan.  
+- (**CAADA**) [Correlation-aware Adversarial Domain Adaptation and Generalization](https://www.sciencedirect.com/science/article/pii/S003132031930425X)
+Rahman, Mohammad Mahfujur, Clinton Fookes, Mahsa Baktashmotlagh, and Sridha Sridharan.
 *Pattern Recognition* (2019): 107124.
 
-- (**CROSSGRAD**) [Generalizing Across Domains via Cross-Gradient Training](https://openreview.net/pdf?id=r1Dx7fbCW)  
-Shankar, Shiv, Vihari Piratla, Soumen Chakrabarti, Siddhartha Chaudhuri, Preethi Jyothi, and Sunita Sarawagi.  
+- (**CROSSGRAD**) [Generalizing Across Domains via Cross-Gradient Training](https://openreview.net/pdf?id=r1Dx7fbCW)
+Shankar, Shiv, Vihari Piratla, Soumen Chakrabarti, Siddhartha Chaudhuri, Preethi Jyothi, and Sunita Sarawagi.
 *International Conference on Learning Representations* (**ICLR**) 2018.
 
-- (**MetaReg**) [MetaReg: Towards Domain Generalization using Meta-Regularization](http://papers.nips.cc/paper/7378-metareg-towards-domain-generalization-using-meta-regularization.pdf)  
-Balaji, Yogesh, Swami Sankaranarayanan, and Rama Chellappa.  
+- (**MetaReg**) [MetaReg: Towards Domain Generalization using Meta-Regularization](http://papers.nips.cc/paper/7378-metareg-towards-domain-generalization-using-meta-regularization.pdf)
+Balaji, Yogesh, Swami Sankaranarayanan, and Rama Chellappa.
 *Advances in Neural Information Processing Systems* (**NeurIPS**) 2018.
 
-- (**MLDG**) [Learning to generalize: Meta-learning for domain generalization](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16595/16558)  
-Li, Da, Yongxin Yang, Yi-Zhe Song, and Timothy M. Hospedales.  
-*AAAI Conference on Artificial Intelligence* (**AAAI**) 2018.  
+- (**MLDG**) [Learning to generalize: Meta-learning for domain generalization](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16595/16558)
+Li, Da, Yongxin Yang, Yi-Zhe Song, and Timothy M. Hospedales.
+*AAAI Conference on Artificial Intelligence* (**AAAI**) 2018.
 [[code]](https://github.com/HAHA-DL/MLDG)
 
 #### Kernel-based methods
 
-- (**MDA**) [Domain Generalization via Multidomain Discriminant Analysis](http://auai.org/uai2019/proceedings/papers/101.pdf)  
-Hu, Shoubo, Kun Zhang, Zhitang Chen, Laiwan Chan.  
-*Conference on Uncertainty in Artificial Intelligence* (**UAI**) 2019.  
+- (**MDA**) [Domain Generalization via Multidomain Discriminant Analysis](http://auai.org/uai2019/proceedings/papers/101.pdf)
+Hu, Shoubo, Kun Zhang, Zhitang Chen, Laiwan Chan.
+*Conference on Uncertainty in Artificial Intelligence* (**UAI**) 2019.
 [[code]](https://github.com/amber0309/Multidomain-Discriminant-Analysis)
 
-- (**CIDG**) [Domain Generalization via Conditional Invariant Representation](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16595/16558)  
-Li, Ya, Mingming Gong, Xinmei Tian, Tongliang Liu, and Dacheng Tao.  
-*AAAI Conference on Artificial Intelligence* (**AAAI**) 2018.  
+- (**CIDG**) [Domain Generalization via Conditional Invariant Representation](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16595/16558)
+Li, Ya, Mingming Gong, Xinmei Tian, Tongliang Liu, and Dacheng Tao.
+*AAAI Conference on Artificial Intelligence* (**AAAI**) 2018.
 [[code]](https://mingming-gong.github.io/papers/CIDG.zip)
 
-- (**SCA**) [Scatter component analysis: A unified framework for domain adaptation and domain generalization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7542175)  
-Ghifary, Muhammad, David Balduzzi, W. Bastiaan Kleijn, and Mengjie Zhang.  
-*IEEE Transactions on Pattern Analysis & Machine Intelligence* (**TPAMI**) 39.7 (2016): 1414-1430.  
+- (**SCA**) [Scatter component analysis: A unified framework for domain adaptation and domain generalization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7542175)
+Ghifary, Muhammad, David Balduzzi, W. Bastiaan Kleijn, and Mengjie Zhang.
+*IEEE Transactions on Pattern Analysis & Machine Intelligence* (**TPAMI**) 39.7 (2016): 1414-1430.
 [[code(unofficial)]](https://github.com/amber0309/SCA)
 
-- (**DICA**) [Domain generalization via invariant feature representation](http://proceedings.mlr.press/v28/muandet13.pdf)  
-Muandet, Krikamol, David Balduzzi, and Bernhard Schölkopf.  
-*International Conference on Machine Learning* (**ICML**) 2013.  
+- (**DICA**) [Domain generalization via invariant feature representation](http://proceedings.mlr.press/v28/muandet13.pdf)
+Muandet, Krikamol, David Balduzzi, and Bernhard Schölkopf.
+*International Conference on Machine Learning* (**ICML**) 2013.
 [[code]](http://krikamol.org/research/codes/dica.zip)
 
 <h3 id="cvbf2021">Computer vision venues</h3>
 
 #### Autoencoder-based methods
 
-- (**MMD-AAE**) [Domain generalization with adversarial feature learning](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/2932.pdf)  
-Li, Haoliang, Sinno Jialin Pan, Shiqi Wang, and Alex C. Kot.  
+- (**MMD-AAE**) [Domain generalization with adversarial feature learning](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/2932.pdf)
+Li, Haoliang, Sinno Jialin Pan, Shiqi Wang, and Alex C. Kot.
 *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2018.
 
-- (**MTAE**) [Domain generalization for object recognition with multi-task autoencoders](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Ghifary_Domain_Generalization_for_ICCV_2015_paper.pdf)  
-Ghifary, Muhammad, W. Bastiaan Kleijn, Mengjie Zhang, and David Balduzzi.  
-*Proceedings of the IEEE International Conference on Computer Vision* (**ICCV**) 2015.  
+- (**MTAE**) [Domain generalization for object recognition with multi-task autoencoders](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Ghifary_Domain_Generalization_for_ICCV_2015_paper.pdf)
+Ghifary, Muhammad, W. Bastiaan Kleijn, Mengjie Zhang, and David Balduzzi.
+*Proceedings of the IEEE International Conference on Computer Vision* (**ICCV**) 2015.
 [[code]](https://github.com/ghif/mtae)
 
 #### Deep neural network-based methods
 
-- (**MetaVIB**) [Learning to Learn with Variational Information Bottleneck for Domain Generalization](https://arxiv.org/abs/2007.07645)  
-Du, Yingjun, Jun Xu, Huan Xiong, Qiang Qiu, Xiantong Zhen, Cees GM Snoek, and Ling Shao.  
-*Proceedings of the European Conference on Computer Vision* (**ECCV**) 2020.  
+- (**MetaVIB**) [Learning to Learn with Variational Information Bottleneck for Domain Generalization](https://arxiv.org/abs/2007.07645)
+Du, Yingjun, Jun Xu, Huan Xiong, Qiang Qiu, Xiantong Zhen, Cees GM Snoek, and Ling Shao.
+*Proceedings of the European Conference on Computer Vision* (**ECCV**) 2020.
 
-- (**DMG**) [Learning to Balance Specificity and Invariance for In and Out of Domain Generalization](https://arxiv.org/abs/2008.12839)  
-Chattopadhyay, Prithvijit, Yogesh Balaji, and Judy Hoffman.  
-*Proceedings of the European Conference on Computer Vision* (**ECCV**) 2020.  
+- (**DMG**) [Learning to Balance Specificity and Invariance for In and Out of Domain Generalization](https://arxiv.org/abs/2008.12839)
+Chattopadhyay, Prithvijit, Yogesh Balaji, and Judy Hoffman.
+*Proceedings of the European Conference on Computer Vision* (**ECCV**) 2020.
 [[code]](https://github.com/prithv1/DMG)
 
-- (**DSON**) [Learning to Optimize Domain Specific Normalization for Domain Generalization](https://arxiv.org/abs/1907.04275)  
-Seonguk Seo, Yumin Suh, Dongwan Kim, Geeho Kim, Jongwoo Han and ohyung Han.  
-*Proceedings of the European Conference on Computer Vision* (**ECCV**) 2020.  
+- (**DSON**) [Learning to Optimize Domain Specific Normalization for Domain Generalization](https://arxiv.org/abs/1907.04275)
+Seonguk Seo, Yumin Suh, Dongwan Kim, Geeho Kim, Jongwoo Han and ohyung Han.
+*Proceedings of the European Conference on Computer Vision* (**ECCV**) 2020.
 
-- (**EISNet**) [Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization](https://arxiv.org/abs/2007.09316)  
-Wang, Shujun, Lequan Yu, Caizi Li, Chi-Wing Fu, and Pheng-Ann Heng.  
-*Proceedings of the European Conference on Computer Vision* (**ECCV**) 2020.  
+- (**EISNet**) [Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization](https://arxiv.org/abs/2007.09316)
+Wang, Shujun, Lequan Yu, Caizi Li, Chi-Wing Fu, and Pheng-Ann Heng.
+*Proceedings of the European Conference on Computer Vision* (**ECCV**) 2020.
 [[code]](https://github.com/EmmaW8/EISNet)
 
-- (**MetaVIB**) [Learning to Learn with Variational Information Bottleneck for Domain Generalization](https://arxiv.org/abs/2007.07645)  
-Du, Yingjun, Jun Xu, Huan Xiong, Qiang Qiu, Xiantong Zhen, Cees GM Snoek, and Ling Shao.  
+- (**MetaVIB**) [Learning to Learn with Variational Information Bottleneck for Domain Generalization](https://arxiv.org/abs/2007.07645)
+Du, Yingjun, Jun Xu, Huan Xiong, Qiang Qiu, Xiantong Zhen, Cees GM Snoek, and Ling Shao.
 *Proceedings of the European Conference on Computer Vision* (**ECCV**) 2020.
 
-- (**RSC**) [Self-Challenging Improves Cross-Domain Generalization](https://arxiv.org/abs/2007.02454)  
-Huang, Zeyi, Haohan Wang, Eric P. Xing, and Dong Huang.  
+- (**RSC**) [Self-Challenging Improves Cross-Domain Generalization](https://arxiv.org/abs/2007.02454)
+Huang, Zeyi, Haohan Wang, Eric P. Xing, and Dong Huang.
 *Proceedings of the European Conference on Computer Vision* (**ECCV**) 2020.
 
-- (**L2A-OT**) [Learning to Generate Novel Domains for Domain Generalization](https://arxiv.org/abs/2007.03304)  
-Zhou, Kaiyang, Yongxin Yang, Timothy Hospedales, and Tao Xiang.  
+- (**L2A-OT**) [Learning to Generate Novel Domains for Domain Generalization](https://arxiv.org/abs/2007.03304)
+Zhou, Kaiyang, Yongxin Yang, Timothy Hospedales, and Tao Xiang.
 *Proceedings of the European Conference on Computer Vision* (**ECCV**) 2020.
 
-- (**SSDG**) [Single-Side Domain Generalization for Face Anti-Spoofing](https://arxiv.org/abs/2004.14043)  
-Jia, Yunpei, Jie Zhang, Shiguang Shan, and Xilin Chen.  
-*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2020.  
+- (**SSDG**) [Single-Side Domain Generalization for Face Anti-Spoofing](https://arxiv.org/abs/2004.14043)
+Jia, Yunpei, Jie Zhang, Shiguang Shan, and Xilin Chen.
+*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2020.
 [[code]](https://github.com/taylover-pei/SSDG-CVPR2020)
 
-- (**Epi-FCR**) [Episodic Training for Domain Generalization](https://arxiv.org/abs/1902.00113)  
-Li, Da, Jianshu Zhang, Yongxin Yang, Cong Liu, Yi-Zhe Song, and Timothy M. Hospedales.  
-*Proceedings of the IEEE International Conference on Computer Vision* (**ICCV**) 2019.  
+- (**Epi-FCR**) [Episodic Training for Domain Generalization](https://arxiv.org/abs/1902.00113)
+Li, Da, Jianshu Zhang, Yongxin Yang, Cong Liu, Yi-Zhe Song, and Timothy M. Hospedales.
+*Proceedings of the IEEE International Conference on Computer Vision* (**ICCV**) 2019.
 [[code]](https://github.com/HAHA-DL/Episodic-DG)
 
-- (**JiGen**) [Domain Generalization by Solving Jigsaw Puzzles](https://arxiv.org/abs/1903.06864)  
-Carlucci, Fabio Maria, Antonio D'Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi.  
-*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2019.  
+- (**JiGen**) [Domain Generalization by Solving Jigsaw Puzzles](https://arxiv.org/abs/1903.06864)
+Carlucci, Fabio Maria, Antonio D'Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi.
+*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2019.
 [[code]](https://github.com/fmcarlucci/JigenDG)
 
-- (**CIDDG**) [Deep Domain Generalization via Conditional Invariant Adversarial Networks](http://openaccess.thecvf.com/content_ECCV_2018/papers/Ya_Li_Deep_Domain_Generalization_ECCV_2018_paper.pdf)  
-Li, Ya, Xinmei Tian, Mingming Gong, Yajing Liu, Tongliang Liu, Kun Zhang, and Dacheng Tao.  
+- (**CIDDG**) [Deep Domain Generalization via Conditional Invariant Adversarial Networks](http://openaccess.thecvf.com/content_ECCV_2018/papers/Ya_Li_Deep_Domain_Generalization_ECCV_2018_paper.pdf)
+Li, Ya, Xinmei Tian, Mingming Gong, Yajing Liu, Tongliang Liu, Kun Zhang, and Dacheng Tao.
 *Proceedings of the European Conference on Computer Vision* (**ECCV**) 2018.
 
-- [Deep Domain Generalization With Structured Low-Rank Constraint](https://ieeexplore.ieee.org/document/8053784)  
-Ding, Zhengming, and Yun Fu.  
+- [Deep Domain Generalization With Structured Low-Rank Constraint](https://ieeexplore.ieee.org/document/8053784)
+Ding, Zhengming, and Yun Fu.
 *IEEE Transactions on Image Processing* (**TIP**) 27.1 (2017): 304-313.
 
-- (**CCSA**) [Unified deep supervised domain adaptation and generalization](http://openaccess.thecvf.com/content_ICCV_2017/papers/Motiian_Unified_Deep_Supervised_ICCV_2017_paper.pdf)  
-Motiian, Saeid, Marco Piccirilli, Donald A. Adjeroh, and Gianfranco Doretto.  
-*Proceedings of the IEEE International Conference on Computer Vision* (**ICCV**) 2017.  
+- (**CCSA**) [Unified deep supervised domain adaptation and generalization](http://openaccess.thecvf.com/content_ICCV_2017/papers/Motiian_Unified_Deep_Supervised_ICCV_2017_paper.pdf)
+Motiian, Saeid, Marco Piccirilli, Donald A. Adjeroh, and Gianfranco Doretto.
+*Proceedings of the IEEE International Conference on Computer Vision* (**ICCV**) 2017.
 [[code]](https://github.com/samotiian/CCSA)
 
-- [Deeper, broader and artier domain generalization](https://ieeexplore.ieee.org/abstract/document/8237853)  
-Li, Da, Yongxin Yang, Yi-Zhe Song, and Timothy M. Hospedales.  
-*Proceedings of the IEEE International Conference on Computer Vision* (**ICCV**) 2017.  
+- [Deeper, broader and artier domain generalization](https://ieeexplore.ieee.org/abstract/document/8237853)
+Li, Da, Yongxin Yang, Yi-Zhe Song, and Timothy M. Hospedales.
+*Proceedings of the IEEE International Conference on Computer Vision* (**ICCV**) 2017.
 [[code]](http://www.eecs.qmul.ac.uk/~dl307/project_iccv2017)
 
 #### Metric learning-based methods
 
-- (**UML**) [Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias](https://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Fang_Unbiased_Metric_Learning_2013_ICCV_paper.pdf)  
-Fang, Chen, Ye Xu, and Daniel N. Rockmore.  
+- (**UML**) [Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias](https://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Fang_Unbiased_Metric_Learning_2013_ICCV_paper.pdf)
+Fang, Chen, Ye Xu, and Daniel N. Rockmore.
 *Proceedings of the IEEE International Conference on Computer Vision* (**ICCV**) 2013.
 
 #### Support vector machine (SVM)-based methods
 
-- (**MVDG**) [Multi-view domain generalization for visual recognition](https://ieeexplore.ieee.org/document/7410834)  
-Niu, Li, Wen Li, and Dong Xu.  
+- (**MVDG**) [Multi-view domain generalization for visual recognition](https://ieeexplore.ieee.org/document/7410834)
+Niu, Li, Wen Li, and Dong Xu.
 *Proceedings of the IEEE International Conference on Computer Vision* (**ICCV**) 2015.
 
-- (**LRE-SVM**) [Exploiting low-rank structure from latent domains for domain generalization](https://link.springer.com/chapter/10.1007/978-3-319-10578-9_41)  
-Xu, Zheng, Wen Li, Li Niu, and Dong Xu.  
-*European Conference on Computer Vision* (**ECCV**) 2014.  
+- (**LRE-SVM**) [Exploiting low-rank structure from latent domains for domain generalization](https://link.springer.com/chapter/10.1007/978-3-319-10578-9_41)
+Xu, Zheng, Wen Li, Li Niu, and Dong Xu.
+*European Conference on Computer Vision* (**ECCV**) 2014.
 [[code]](http://www.vision.ee.ethz.ch/~liwenw/papers/Xu_ECCV2014_codes.zip)
 
-- (**Undo-Bias**) [Undoing the damage of dataset bias](https://link.springer.com/chapter/10.1007/978-3-642-33718-5_12)  
-Khosla, Aditya, Tinghui Zhou, Tomasz Malisiewicz, Alexei A. Efros, and Antonio Torralba.  
-*European Conference on Computer Vision* (**ECCV**) 2012.  
+- (**Undo-Bias**) [Undoing the damage of dataset bias](https://link.springer.com/chapter/10.1007/978-3-642-33718-5_12)
+Khosla, Aditya, Tinghui Zhou, Tomasz Malisiewicz, Alexei A. Efros, and Antonio Torralba.
+*European Conference on Computer Vision* (**ECCV**) 2012.
 [[code]](https://github.com/adikhosla/undoing-bias/archive/master.zip)
 
 <h3 id="arxivbf2021">arXiv</h3>
 
-- (**NILE**) [A causal framework for distribution generalization](https://arxiv.org/abs/2006.07433)    
-Christiansen, Rune, Niklas Pfister, Martin Emil Jakobsen, Nicola Gnecco, and Jonas Peters.  
-*arXiv preprint arXiv:2006.07433* (2020).  
+- (**NILE**) [A causal framework for distribution generalization](https://arxiv.org/abs/2006.07433)
+Christiansen, Rune, Niklas Pfister, Martin Emil Jakobsen, Nicola Gnecco, and Jonas Peters.
+*arXiv preprint arXiv:2006.07433* (2020).
 
-- (**REx**) [Out-of-distribution generalization via risk extrapolation](https://arxiv.org/abs/2003.00688)    
-Krueger, David, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Remi Le Priol, and Aaron Courville.  
-*arXiv preprint arXiv:2003.00688* (2020).  
+- (**REx**) [Out-of-distribution generalization via risk extrapolation](https://arxiv.org/abs/2003.00688)
+Krueger, David, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Remi Le Priol, and Aaron Courville.
+*arXiv preprint arXiv:2003.00688* (2020).
 
-- (**RVP**) [Risk Variance Penalization: From Distributional Robustness to Causality](https://arxiv.org/abs/2006.07544)    
-Xie, Chuanlong, Fei Chen, Yue Liu, and Zhenguo Li.    
-*arXiv preprint arXiv:2006.07544* (2020).  
+- (**RVP**) [Risk Variance Penalization: From Distributional Robustness to Causality](https://arxiv.org/abs/2006.07544)
+Xie, Chuanlong, Fei Chen, Yue Liu, and Zhenguo Li.
+*arXiv preprint arXiv:2006.07544* (2020).
 
-- [Generalization and Invariances in the Presence of Unobserved Confounding](https://arxiv.org/abs/2007.10653)  
-Bellot, Alexis and van der Schaar, Mihaela.  
-*arXiv preprint arXiv:2007.10653* (2020).  
+- [Generalization and Invariances in the Presence of Unobserved Confounding](https://arxiv.org/abs/2007.10653)
+Bellot, Alexis and van der Schaar, Mihaela.
+*arXiv preprint arXiv:2007.10653* (2020).
 
-- (**FAR**) [Feature Alignment and Restoration for Domain Generalization and Adaptation](https://arxiv.org/abs/2006.12009)  
-Jin, Xin, Cuiling Lan, Wenjun Zeng, and Zhibo Chen.  
-*arXiv preprint arXiv:2006.12009* (2020).  
+- (**FAR**) [Feature Alignment and Restoration for Domain Generalization and Adaptation](https://arxiv.org/abs/2006.12009)
+Jin, Xin, Cuiling Lan, Wenjun Zeng, and Zhibo Chen.
+*arXiv preprint arXiv:2006.12009* (2020).
 
-- [Frustratingly Simple Domain Generalization via Image Stylization](https://arxiv.org/abs/2006.11207)  
-Somavarapu, Nathan, Chih-Yao Ma, and Zsolt Kira.  
-*arXiv preprint arXiv:2006.11207* (2020).  
+- [Frustratingly Simple Domain Generalization via Image Stylization](https://arxiv.org/abs/2006.11207)
+Somavarapu, Nathan, Chih-Yao Ma, and Zsolt Kira.
+*arXiv preprint arXiv:2006.11207* (2020).
 [[code]](https://github.com/GT-RIPL/DomainGeneralization-Stylization)
 
-- (**RVR**) [Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations](https://arxiv.org/abs/2006.11478)  
-Deng, Zhun, Frances Ding, Cynthia Dwork, Rachel Hong, Giovanni Parmigiani, Prasad Patil, and Pragya Sur.  
-*arXiv preprint arXiv:2006.11478* (2020).  
+- (**RVR**) [Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations](https://arxiv.org/abs/2006.11478)
+Deng, Zhun, Frances Ding, Cynthia Dwork, Rachel Hong, Giovanni Parmigiani, Prasad Patil, and Pragya Sur.
+*arXiv preprint arXiv:2006.11478* (2020).
 
-- (**G2DM**) [Generalizing to unseen Domains via Distribution Matching](https://arxiv.org/abs/1911.00804)  
-Isabela Albuquerque, João Monteiro, Mohammad Darvishi, Tiago H. Falk, Ioannis Mitliagkas  
-*arXiv preprint arXiv:1911.00804* (2019).  
+- (**G2DM**) [Generalizing to unseen Domains via Distribution Matching](https://arxiv.org/abs/1911.00804)
+Isabela Albuquerque, João Monteiro, Mohammad Darvishi, Tiago H. Falk, Ioannis Mitliagkas
+*arXiv preprint arXiv:1911.00804* (2019).
 [[code]](https://github.com/belaalb/TI-DG)
 
-- [Invariant Risk Minimization](https://arxiv.org/abs/1907.02893)  
-Arjovsky, Martin and Bottou, Leon and Gulrajani, Ishaan and Lopez-Paz, David.  
-*arXiv preprint arXiv:1907.02893* (2019).  
+- [Invariant Risk Minimization](https://arxiv.org/abs/1907.02893)
+Arjovsky, Martin and Bottou, Leon and Gulrajani, Ishaan and Lopez-Paz, David.
+*arXiv preprint arXiv:1907.02893* (2019).
 [[code]](https://github.com/facebookresearch/InvariantRiskMinimization)
 
-- [A Generalization Error Bound for Multi-class Domain Generalization](https://arxiv.org/abs/1905.10392)  
-Deshmukh, Aniket Anand, Yunwen Lei, Srinagesh Sharma, Urun Dogan, James W. Cutler, and Clayton Scott.  
-*arXiv preprint arXiv:1905.10392* (2019).  
+- [A Generalization Error Bound for Multi-class Domain Generalization](https://arxiv.org/abs/1905.10392)
+Deshmukh, Aniket Anand, Yunwen Lei, Srinagesh Sharma, Urun Dogan, James W. Cutler, and Clayton Scott.
+*arXiv preprint arXiv:1905.10392* (2019).
 [[code]](https://www.dropbox.com/sh/bls758ro5762mtf/AACbn3UXJItY9uwtmCAdi7E3a?dl=0)
 
-- [Domain generalization by marginal transfer learning](https://arxiv.org/abs/1711.07910)  
-Blanchard, Gilles, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, and Clayton Scott.  
-*arXiv preprint arXiv:1711.07910* (2017).  
+- [Domain generalization by marginal transfer learning](https://arxiv.org/abs/1711.07910)
+Blanchard, Gilles, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, and Clayton Scott.
+*arXiv preprint arXiv:1711.07910* (2017).
 [[code]](https://github.com/aniketde/DomainGeneralizationMarginal)
 
 -----
@@ -389,22 +394,22 @@ Blanchard, Gilles, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, and Clayton Sc
 
 #### Introduction
 
-This dataset is constructed by collecting common classes in two datasets: Office-31 (which contains A, W and D) and Caltech-256 (which is C).  
-Four domains: A(Amazon, 958 instances), W(Webcam, 295 instances), D(DSLR, 157 instances), and C(Caltech, 1123 instances).  
+This dataset is constructed by collecting common classes in two datasets: Office-31 (which contains A, W and D) and Caltech-256 (which is C).
+Four domains: A(Amazon, 958 instances), W(Webcam, 295 instances), D(DSLR, 157 instances), and C(Caltech, 1123 instances).
 Ten common classes: back pack, bike, calculator, headphones, keyboard, laptop_computer, monitor, mouse, mug, and projector.
 
 #### Download
 
-Download Office+Caltech original images [[Google Drive](https://drive.google.com/file/d/14OIlzWFmi5455AjeBZLak2Ku-cFUrfEo/view?usp=sharing)]  
-Download Office+Caltech SURF dataset [[Google Drive](https://drive.google.com/file/d/1TKot-lmTy5h797YaAeydkOD6kWqii5fa/view?usp=sharing)]  
+Download Office+Caltech original images [[Google Drive](https://drive.google.com/file/d/14OIlzWFmi5455AjeBZLak2Ku-cFUrfEo/view?usp=sharing)]
+Download Office+Caltech SURF dataset [[Google Drive](https://drive.google.com/file/d/1TKot-lmTy5h797YaAeydkOD6kWqii5fa/view?usp=sharing)]
 Download Office+Caltech DeCAF dataset [[Google Drive](https://drive.google.com/file/d/1mgEyml0ZoZjUlUQfWNfr-Srxmlot3yq6/view?usp=sharing)]
 
 ### VLCS
 
 #### Introduction
 
-Four domains: V(VOC2007), L(LabelMe), C(Caltech), and S(SUN09).  
-Five common classes: bird, car, chair, dog, and person. 
+Four domains: V(VOC2007), L(LabelMe), C(Caltech), and S(SUN09).
+Five common classes: bird, car, chair, dog, and person.
 
 #### Download
 
@@ -433,8 +438,8 @@ Download links are available at https://github.com/hendrycks/imagenet-r
 
 #### Introduction
 
-Four domains: photo, art painting, cartoon, and sketch.  
-Seven common classes: dog, elephant, horse, giraffe, guitar, house, and person. 
+Four domains: photo, art painting, cartoon, and sketch.
+Seven common classes: dog, elephant, horse, giraffe, guitar, house, and person.
 
 #### Download
 
@@ -444,7 +449,7 @@ Download the PACS dataset [[Google Drive](https://drive.google.com/drive/folders
 
 #### Introduction
 
-This dataset contains a subset of the popular YFCC100M dataset, that are partitioned based on the images' country of origin. 
+This dataset contains a subset of the popular YFCC100M dataset, that are partitioned based on the images' country of origin.
 
 #### Download
 
@@ -455,38 +460,38 @@ The infomation of Geo-YFCC dataset is available at https://github.com/abhimanyud
 
 ## DG variants
 
-- (**RaMoE**) [Generalizable Person Re-identification with Relevance-aware Mixture of Experts](https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_Generalizable_Person_Re-Identification_With_Relevance-Aware_Mixture_of_Experts_CVPR_2021_paper.pdf)  
-Dai, Yongxing, Xiaotong Li, Jun Liu, Zekun Tong, and Ling-Yu Duan.  
-*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.  
+- (**RaMoE**) [Generalizable Person Re-identification with Relevance-aware Mixture of Experts](https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_Generalizable_Person_Re-Identification_With_Relevance-Aware_Mixture_of_Experts_CVPR_2021_paper.pdf)
+Dai, Yongxing, Xiaotong Li, Jun Liu, Zekun Tong, and Ling-Yu Duan.
+*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2021.
 
-- [Zero Shot Domain Generalization](https://arxiv.org/abs/2008.07443)  
-Udit Maniyar, Joseph K J, Aniket Anand Deshmukh, Urun Dogan, Vineeth N Balasubramanian  
-*British Machine Vision Conference* (**BMVC**) 2020.  
+- [Zero Shot Domain Generalization](https://arxiv.org/abs/2008.07443)
+Udit Maniyar, Joseph K J, Aniket Anand Deshmukh, Urun Dogan, Vineeth N Balasubramanian
+*British Machine Vision Conference* (**BMVC**) 2020.
 
-- [Exchanging Lessons Between Algorithmic Fairness and Domain Generalization](https://arxiv.org/abs/2010.07249)  
-Creager, Elliot, Jörn-Henrik Jacobsen, and Richard Zemel.  
-*arXiv preprint arXiv:2010.07249* 2020.  
+- [Exchanging Lessons Between Algorithmic Fairness and Domain Generalization](https://arxiv.org/abs/2010.07249)
+Creager, Elliot, Jörn-Henrik Jacobsen, and Richard Zemel.
+*arXiv preprint arXiv:2010.07249* 2020.
 
-- [Learning to Learn Single Domain Generalization](https://arxiv.org/abs/2003.13216)  
-Fengchun Qiao, Long Zhao, Xi Peng.  
-*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2020.  
+- [Learning to Learn Single Domain Generalization](https://arxiv.org/abs/2003.13216)
+Fengchun Qiao, Long Zhao, Xi Peng.
+*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (**CVPR**) 2020.
 
-- [Domain Generalization Using a Mixture of Multiple Latent Domains](https://aaai.org/Papers/AAAI/2020GB/AAAI-MatsuuraT.3100.pdf)  
-Toshihiko Matsuura, Tatsuya Harada.  
-*AAAI Conference on Artificial Intelligence* (**AAAI**) 2020.  
+- [Domain Generalization Using a Mixture of Multiple Latent Domains](https://aaai.org/Papers/AAAI/2020GB/AAAI-MatsuuraT.3100.pdf)
+Toshihiko Matsuura, Tatsuya Harada.
+*AAAI Conference on Artificial Intelligence* (**AAAI**) 2020.
 [[code]](https://github.com/mil-tokyo/dg_mmld)
 
-- (**APN**) [Adversarial Pyramid Network for Video Domain Generalization](https://arxiv.org/abs/1912.03716)  
-Zhiyu Yao, Yunbo Wang, Xingqiang Du, Mingsheng Long, Jianmin Wang  
-*arXiv preprint arXiv:1912.03716* (2019).  
+- (**APN**) [Adversarial Pyramid Network for Video Domain Generalization](https://arxiv.org/abs/1912.03716)
+Zhiyu Yao, Yunbo Wang, Xingqiang Du, Mingsheng Long, Jianmin Wang
+*arXiv preprint arXiv:1912.03716* (2019).
 
-- (**FC**) [Feature-Critic Networks for Heterogeneous Domain Generalization](https://arxiv.org/abs/1901.11448)  
-Li, Yiying, Yongxin Yang, Wei Zhou, and Timothy M. Hospedales  
-*International Conference on Machine Learning* (**ICML**) 2019.  
+- (**FC**) [Feature-Critic Networks for Heterogeneous Domain Generalization](https://arxiv.org/abs/1901.11448)
+Li, Yiying, Yongxin Yang, Wei Zhou, and Timothy M. Hospedales
+*International Conference on Machine Learning* (**ICML**) 2019.
 [[code]](https://github.com/liyiying/Feature_Critic)
 
-- [Learning Robust Representations by Projecting Superficial Statistics Out](https://openreview.net/pdf?id=rJEjjoR9K7)  
-Wang, Haohan, Zexue He, Zachary C. Lipton, and Eric P. Xing.  
+- [Learning Robust Representations by Projecting Superficial Statistics Out](https://openreview.net/pdf?id=rJEjjoR9K7)
+Wang, Haohan, Zexue He, Zachary C. Lipton, and Eric P. Xing.
 *International Conference on Learning Representations* (**ICLR**) 2019.
 
 -----