Understanding unsupervised domain adaption

理解无监督域适应方法:

https://zhuanlan.zhihu.com/p/27903002
Deep Domain Confusion: Maximizing for Domain Invariance

http://cseweb.ucsd.edu/classes/sp18/cse252C-a/CSE252C_20180430.pdf
Unsupervised Domain Adaptation by Backpropagation

https://paperswithcode.com/paper/bridging-theory-and-algorithm-for-domain
Bridging Theory and Algorithm for Domain Adaptation

https://paperswithcode.com/paper/less-confusion-more-transferable-minimum
Minimum Class Confusion for Versatile Domain Adaptation(ECCV2020)

https://paperswithcode.com/paper/adversarial-transfer-learning
A Survey of Unsupervised Deep Domain Adaptation(2018)

https://paperswithcode.com/paper/fda-fourier-domain-adaptation-for-semantic
FDA: Fourier Domain Adaptation for Semantic Segmentation (CVPR2020)

https://paperswithcode.com/paper/a-balanced-and-uncertainty-aware-approach-for
A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation (ECCV2020)

 

无监督域适应4目标检测(2D):

https://paperswithcode.com/paper/domain-adaptive-faster-r-cnn-for-object
Domain Adaptive Faster R-CNN for Object Detection in the Wild (CVPR2018)

Understanding unsupervised domain adaption_第1张图片

https://paperswithcode.com/paper/exploring-categorical-regularization-for
Exploring Categorical Regularization for Domain Adaptive Object Detection (CVPR2020)

https://paperswithcode.com/paper/domain-adaptive-object-detection-via-1#code
Domain-Adaptive Object Detection via Uncertainty-Aware Distribution Alignment

Understanding unsupervised domain adaption_第2张图片

https://paperswithcode.com/paper/progressive-domain-adaptation-for-object
Progressive Domain Adaptation for Object Detection

Understanding unsupervised domain adaption_第3张图片

 

无监督域适应4目标检测(3D):

https://paperswithcode.com/paper/st3d-self-training-for-unsupervised-domain
ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection

https://paperswithcode.com/paper/pv-rcnn-the-top-performing-lidar-only
PV-RCNN: The Top-Performing LiDAR-only Solutions for 3D Detection / 3D Tracking / Domain Adaptation of Waymo Open Dataset Challenges

 

无监督域适应4景深估计:

https://paperswithcode.com/paper/geometry-aware-symmetric-domain-adaptation
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation (CVPR2019)

https://paperswithcode.com/paper/real-time-monocular-depth-estimation-using
Real-Time Monocular Depth Estimation Using Synthetic Data With Domain Adaptation via Image Style Transfer (CVPR2018)

https://paperswithcode.com/paper/sim2real-for-self-supervised-monocular-depth
Sim2Real for Self-Supervised Monocular Depth and Segmentation (2020)

 

无监督域适应4语义分割:

https://paperswithcode.com/paper/differential-treatment-for-stuff-and-things-a
Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation (CVPR2020)

 

https://paperswithcode.com/paper/unsupervised-intra-domain-adaptation-for
Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision (CVPR2020)

https://paperswithcode.com/paper/unsupervised-scene-adaptation-with-memory
Unsupervised Scene Adaptation with Memory Regularization in vivo

https://paperswithcode.com/paper/rectifying-pseudo-label-learning-via
Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation

https://paperswithcode.com/paper/dada-depth-aware-domain-adaptation-in
DADA: Depth-aware Domain Adaptation in Semantic Segmentation (ICCV2019)

https://paperswithcode.com/paper/learning-to-adapt-structured-output-space-for
Learning to Adapt Structured Output Space for Semantic Segmentation (CVPR2018)

https://paperswithcode.com/paper/domain-adaptation-for-structured-output-via
Domain Adaptation for Structured Output via Discriminative Patch Representations (ICCV2019)

https://paperswithcode.com/paper/fda-fourier-domain-adaptation-for-semantic
FDA: Fourier Domain Adaptation for Semantic Segmentation (CVPR2020)


无监督域适应4计算成像:

https://paperswithcode.com/paper/domain-adaptation-for-image-dehazing
Domain Adaptation for Image Dehazing (CVPR2020)

 

 

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