Domain Adaptation in Computer Vision Applications /
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| Formato: | eBook |
| Lenguaje: | English |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2017.
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| Edición: | 1st ed. 2017. |
| Colección: | Advances in Computer Vision and Pattern Recognition,
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| Materias: |
Tabla de Contenidos:
- 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.