Dr. Gabriela Csurka is a Senior Scientist in the Computer Vision Team at Naver Labs Europe, Meylan, France.

• A Comprehensive Survey on Domain Adaptation for Visual Applications.- A Deeper Look at Dataset Bias.- Part I: Shallow Domain Adaptation Methods.- Geodesic Flow Kernel and Landmarks: Kernel Methods for Unsupervised Domain Adaptation.- Unsupervised Domain Adaptation based on Subspace Alignment.- Learning Domain Invariant Embeddings by Matching Distributions.- Adaptive Transductive Transfer Machines: A Pipeline for Unsupervised Domain Adaptation.- What To Do When the Access to the Source Data is Constrained?.- Part II: Deep Domain Adaptation Methods.- Correlation Alignment for Unsupervised Domain Adaptation.- Simultaneous Deep Transfer Across Domains and Tasks.- Domain-Adversarial Training of Neural Networks.- Part III: Beyond Image Classification.- Unsupervised Fisher Vector Adaptation for Re-Identification.- Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA.- From Virtual to Real World Visual Perception using Domain Adaptation – The DPM as Example.- Generalizing Semantic Part Detectors Across Domains.- Part IV: Beyond Domain Adaptation: Unifying Perspectives.- A Multi-Source Domain Generalization Approach to Visual Attribute Detection.- Unifying Multi-Domain Multi-Task Learning: Tensor and Neural Network Perspectives.

Dr. Gabriela Csurka is a Senior Scientist in the Computer Vision Team at Naver Labs Europe, Meylan, France.

• A Comprehensive Survey on Domain Adaptation for Visual Applications.- A Deeper Look at Dataset Bias.- Part I: Shallow Domain Adaptation Methods.- Geodesic Flow Kernel and Landmarks: Kernel Methods for Unsupervised Domain Adaptation.- Unsupervised Domain Adaptation based on Subspace Alignment.- Learning Domain Invariant Embeddings by Matching Distributions.- Adaptive Transductive Transfer Machines: A Pipeline for Unsupervised Domain Adaptation.- What To Do When the Access to the Source Data is Constrained?.- Part II: Deep Domain Adaptation Methods.- Correlation Alignment for Unsupervised Domain Adaptation.- Simultaneous Deep Transfer Across Domains and Tasks.- Domain-Adversarial Training of Neural Networks.- Part III: Beyond Image Classification.- Unsupervised Fisher Vector Adaptation for Re-Identification.- Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA.- From Virtual to Real World Visual Perception using Domain Adaptation – The DPM as Example.- Generalizing Semantic Part Detectors Across Domains.- Part IV: Beyond Domain Adaptation: Unifying Perspectives.- A Multi-Source Domain Generalization Approach to Visual Attribute Detection.- Unifying Multi-Domain Multi-Task Learning: Tensor and Neural Network Perspectives.

Dr. Gabriela Csurka is a Senior Scientist in the Computer Vision Team at Naver Labs Europe, Meylan, France.

• A Comprehensive Survey on Domain Adaptation for Visual Applications.- A Deeper Look at Dataset Bias.- Part I: Shallow Domain Adaptation Methods.- Geodesic Flow Kernel and Landmarks: Kernel Methods for Unsupervised Domain Adaptation.- Unsupervised Domain Adaptation based on Subspace Alignment.- Learning Domain Invariant Embeddings by Matching Distributions.- Adaptive Transductive Transfer Machines: A Pipeline for Unsupervised Domain Adaptation.- What To Do When the Access to the Source Data is Constrained?.- Part II: Deep Domain Adaptation Methods.- Correlation Alignment for Unsupervised Domain Adaptation.- Simultaneous Deep Transfer Across Domains and Tasks.- Domain-Adversarial Training of Neural Networks.- Part III: Beyond Image Classification.- Unsupervised Fisher Vector Adaptation for Re-Identification.- Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA.- From Virtual to Real World Visual Perception using Domain Adaptation – The DPM as Example.- Generalizing Semantic Part Detectors Across Domains.- Part IV: Beyond Domain Adaptation: Unifying Perspectives.- A Multi-Source Domain Generalization Approach to Visual Attribute Detection.- Unifying Multi-Domain Multi-Task Learning: Tensor and Neural Network Perspectives.