Merge pull request #96 from tsingcbx99/master

Fix Typo

Author: JunguangJiang

README.md

@@ -36,10 +36,9 @@ The currently supported algorithms include:
 - [Regressive Domain Adaptation for Unsupervised Keypoint Detection (RegDA, CVPR 2021)](http://ise.thss.tsinghua.edu.cn/~mlong/doc/regressive-domain-adaptation-cvpr21.pdf)
 
 ##### [Domain Adaptation for Person Re-identification](/examples/domain_adaptation/re_identification/)
-- [Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net},  
-  booktitle (IBN-Net, 2018 ECCV)](https://openaccess.thecvf.com/content_ECCV_2018/papers/Xingang_Pan_Two_at_Once_ECCV_2018_paper.pdf)
-- [Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification (MMT, 2020 ICLR)](https://arxiv.org/abs/2001.01526)
-- [Similarity Preserving Generative Adversarial Network (SPGAN)](https://arxiv.org/pdf/1811.10551.pdf)
+- [Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net (IBN-Net, ECCV 2018)](https://openaccess.thecvf.com/content_ECCV_2018/papers/Xingang_Pan_Two_at_Once_ECCV_2018_paper.pdf)
+- [Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification (MMT, ICLR 2020)](https://arxiv.org/abs/2001.01526)
+- [Similarity Preserving Generative Adversarial Network (SPGAN, CVPR 2018)](https://arxiv.org/pdf/1811.10551.pdf)
 
 ##### [Task Adaptation for Image Classification](/examples/task_adaptation/image_classification/)
 - [Explicit inductive bias for transfer learning with convolutional networks
@@ -52,19 +51,17 @@ The currently supported algorithms include:
 - [Bi-tuning of Pre-trained Representations (Bi-Tuning)](https://arxiv.org/abs/2011.06182?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+arxiv%2FQSXk+%28ExcitingAds%21+cs+updates+on+arXiv.org%29)
 
 ##### [Domain Generalization for Classification](/examples/domain_generalization/image_classification/)
-- [Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net},  
-  booktitle (IBN-Net, 2018 ECCV)](https://openaccess.thecvf.com/content_ECCV_2018/papers/Xingang_Pan_Two_at_Once_ECCV_2018_paper.pdf)
-- [Domain Generalization with MixStyle (MixStyle, 2021 ICLR)](https://arxiv.org/abs/2104.02008)
-- [Learning to Generalize: Meta-Learning for Domain Generalization (MLDG, 2018 AAAI)](https://arxiv.org/pdf/1710.03463.pdf)
+- [Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net (IBN-Net, ECCV 2018)](https://openaccess.thecvf.com/content_ECCV_2018/papers/Xingang_Pan_Two_at_Once_ECCV_2018_paper.pdf)
+- [Domain Generalization with MixStyle (MixStyle, ICLR 2021)](https://arxiv.org/abs/2104.02008)
+- [Learning to Generalize: Meta-Learning for Domain Generalization (MLDG, AAAI 2018)](https://arxiv.org/pdf/1710.03463.pdf)
 - [Invariant Risk Minimization (IRM)](https://arxiv.org/abs/1907.02893)
-- [Out-of-Distribution Generalization via Risk Extrapolation (REx) (VREx, ICML 2021)](https://arxiv.org/abs/2003.00688)
+- [Out-of-Distribution Generalization via Risk Extrapolation (VREx, ICML 2021)](https://arxiv.org/abs/2003.00688)
 - [Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization(GroupDRO)](https://arxiv.org/abs/1911.08731)
-- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation (Deep Coral, 2016 ECCV)](https://arxiv.org/abs/1607.01719)
+- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation (Deep Coral, ECCV 2016)](https://arxiv.org/abs/1607.01719)
 
 ##### [Domain Generalization for Person Re-identification](/examples/domain_generalization/re_identification/)
-- [Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net},  
-  booktitle (IBN-Net, 2018 ECCV)](https://openaccess.thecvf.com/content_ECCV_2018/papers/Xingang_Pan_Two_at_Once_ECCV_2018_paper.pdf)
-- [Domain Generalization with MixStyle (MixStyle, 2021 ICLR)](https://arxiv.org/abs/2104.02008)
+- [Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net (IBN-Net, ECCV 2018)](https://openaccess.thecvf.com/content_ECCV_2018/papers/Xingang_Pan_Two_at_Once_ECCV_2018_paper.pdf)
+- [Domain Generalization with MixStyle (MixStyle, ICLR 2021)](https://arxiv.org/abs/2104.02008)
 
 We are planning to add
 - DA for Object Detection

dalib/adaptation/adda.py

@@ -14,7 +14,8 @@ class DomainAdversarialLoss(nn.Module):
     <https://arxiv.org/pdf/1702.05464.pdf>`_.
     Similar to the original `GAN <https://arxiv.org/pdf/1406.2661.pdf>`_ paper, ADDA argues that replacing
     :math:`\text{log}(1-p)` with :math:`-\text{log}(p)` in the adversarial loss provides better gradient qualities. Detailed
-    optimization process can be found at `examples/domain_adaptation/image_classification/adda.py`.
+    optimization process can be found `here
+    <https://github.com/thuml/Transfer-Learning-Library/blob/master/examples/domain_adaptation/image_classification/adda.py>`_.
 
     Inputs:
         - domain_pred (tensor): predictions of domain discriminator

docs/dalib/adaptation/domain_adversarial.rst

@@ -34,8 +34,8 @@ ADDA: Adversarial Discriminative Domain Adaptation
 .. autoclass:: dalib.adaptation.adda.DomainAdversarialLoss
 
 .. note::
-    ADDAgrl is also implemented and benchmarked. You can find code at
-    `examples/domain_adaptation/image_classification/addagrl.py`.
+    ADDAgrl is also implemented and benchmarked. You can find code
+    `here <https://github.com/thuml/Transfer-Learning-Library/blob/master/examples/domain_adaptation/image_classification/addagrl.py>`_.
 
 
 .. _BSP:

examples/domain_adaptation/re_identification/README.md

@@ -60,6 +60,13 @@ If you use these methods in your research, please consider citing.
     year = {2018}  
 }
 
+@inproceedings{SPGAN,
+    title={Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification},
+    author={Deng, Weijian and Zheng, Liang and Ye, Qixiang and Kang, Guoliang and Yang, Yi and Jiao, Jianbin},
+    booktitle={CVPR},
+    year={2018}
+}
+
 @inproceedings{
     MMT,
     title={Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification},

examples/domain_generalization/image_classification/README.md

@@ -72,8 +72,8 @@ If you use these methods in your research, please consider citing.
 @inproceedings{MLDG,
     title={Learning to Generalize: Meta-Learning for Domain Generalization},
     author={Li, Da and Yang, Yongxin and Song, Yi-Zhe and Hospedales, Timothy},
-  	booktitle={AAAI Conference on Artificial Intelligence},
-  	year={2018}
+    booktitle={AAAI},
+    year={2018}
 }
  
 @misc{IRM,