Domain Adaptation in Computer Vision Applications
This comprehensive text/reference presents a broad review of diverse domain adaptation (DA) methods for machine learning, with a focus on solutions for visual applications. The book collects together solutions and perspectives proposed by an international selection of pre-eminent experts in the fiel...
| Συγγραφή απο Οργανισμό/Αρχή: | |
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| Άλλοι συγγραφείς: | |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Cham :
Springer International Publishing : Imprint: Springer,
2017.
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| Σειρά: | Advances in Computer Vision and Pattern Recognition,
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| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.
