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Synthetic-to-Real Translation
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Synthetic-to-real translation is the task of domain adaptation from synthetic (or virtual) data to real data.
<span style="color:grey; opacity: 0.6">( Image credit: CYCADA )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation
Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image Classification
Instance Adaptive Self-Training for Unsupervised Domain Adaptation
Learning from Scale-Invariant Examples for Domain Adaptation in Semantic Segmentation
DACS: Domain Adaptation via Cross-domain Mixed Sampling
Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation
StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching
Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
Deep CG2Real: Synthetic-to-Real Translation via Image Disentanglement
Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation
Unsupervised Scene Adaptation with Memory Regularization in vivo
Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation
Discriminative Adversarial Domain Adaptation
Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation
MLSL: Multi-Level Self-Supervised Learning for Domain Adaptation with Spatially Independent and Semantically Consistent Labeling
Unsupervised Domain Adaptation through Self-Supervision
Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach
Confidence Regularized Self-Training
Bidirectional Learning for Domain Adaptation of Semantic Segmentation
Switchable Whitening for Deep Representation Learning
All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach
Domain Adaptation for Structured Output via Discriminative Patch Representations
A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training
Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation
Diverse Image-to-Image Translation via Disentangled Representations
Learning to Adapt Structured Output Space for Semantic Segmentation
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Adversarial Dropout Regularization
Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes
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