Domain Adaptation 2019 Conference Papers

本文主要是介绍Domain Adaptation 2019 Conference Papers，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

Abbreviation	Paper Title	Source Link	Code	Tags
DTA	Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation	ICCV2019	PyTorch(Official)	DTA Adversarial Dropout
BSP	Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation	ICML2019	Pytorch(Official)	LDA-SVD->BSP Between-class Within-class
DEV	Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation	ICML2019	sklearn(Official)	Density-Ratio-Estimation Variance-Reduction
Zhao’s	On Learning Invariant Representation for Domain Adaptation	ICML2019	Code(empty)	Theory Conditional-Shfit Information-Theoretic-Lower-Bound
Wu’s	Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment	ICML2019		Theory Label-Shift Asymmetrically-Relaxed-Distances
MDD	Bridging Theory and Algorithm for Domain Adaptation	ICML2019	Pytorch(Official)	Theory Margin-Disparity-Discrepancy Rademacher-Complexity
CADA	Attending to Discriminative Certainty for Domain Adaptation	CVPR2019 arXiv	Code(Empty)	Region-Adaptation Bayesian-Framework Attention
d-SNE	d-SNE: Domain Adaptation Using Stochastic Neighborhood Embedding	CVPR2019 Oral arXiv	MXNet-Gluon(Official)	Hausdorff-Distance Domain-Generalization
GCAN	GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation	CVPR2019		Structureaware-Alignment Domain-Alignment Class-Centroid-Alignment
GIO-Ada	Learning Semantic Segmentation From Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach	CVPR2019		Geometric-Information Adversarial-Training Depth-and-Semantic-Prediction
DISE	All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation	CVPR2019	Pytorch(Official)	Domain-Invariant-Structure Domain-Specific-Representations
DSBN	Domain-Specific Batch Normalization for Unsupervised Domain Adaptation	CVPR2019		Batch-Normalization Pseudo-Labels
DWT	Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss	CVPR2019		Min-Entropy Consensus loss Domain-Alignment-Layer
BDL	Bidirectional Learning for Domain Adaptation of Semantic Segmentation	CVPR2019	Pytorch(Official)	Image-Translation Alternative Learning Perceptual-Loss
CAN	Contrastive Adaptation Network for Unsupervised Domain Adaptation	CVPR2019		Intra-Class-Discrepancy Inter-Class-Discrepancy CDD-Metric
GPDA	Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach	CVPR2019 Oral		MCD->GP Classifier’s-Posterior-Distribution
Tran’s	Joint Pixel and Feature-level Domain Adaptation in the Wild	CVPR2019		Combining-Many-Method
UAN	Universal Domain Adaptation	CVPR2019		Sample-Level Partial and Open Set
ADVENT	ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation	CVPR2019 Oral	Code(Empty)	Meta-Sub-Target
AMEAN	Blending-Target Domain Adaptation by Adversarial Meta-Adaptation Networks	CVPR2019 Oral	Pytorch(Official)	Multiple Sub-targets Category-Misalignment
TPN	Transferrable Prototypical Networks for Unsupervised Domain Adaptation	CVPR2019 Oral		Non-linear-Mapping Pseudo-Label Score-Distribution
PFAN	Progressive Feature Alignment for Unsupervised Domain Adaptation	CVPR2019		Intra-Class-Variation Adaptive-Prototype-Alignment Non-Saturated-Classifier
SymNets	Domain-Symmetric Networks for Adversarial Domain Adaptation	CVPR2019	Pytorch(Official)	Symmetric-Classifiers Domain-Confusion Category-Level
CLAN	Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation	CVPR2019 Oral	Pytorch(Official)	Category-Level Co-training
SWD	Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation	CVPR2019		Wasserstein-Discrepancy

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