
Visual Domain Adaptation in the Deep Learning Era
Description
Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.
More details
Other editions
Additional editions

Persons
Content
- Cover
- Copyright Page
- Title Page
- Contents
- Preface
- Figure Credits
- Motivation
- Introduction
- Real Application Need
- Dataset Bias
- DA Benchmarks and Challenges
- Book Organization
- Theoretical Background
- Basic Concepts
- Cross-Domain Generalization
- Distances Between Distributions
- Maximum Mean Discrepancy
- f-Divergences
- Wasserstein Distance
- Traditional Methods
- Parameter Adaptation for DA
- Metric Learning for DA
- Aligning Data Representations
- Subspace Representations
- Other Representations
- Aligning Data Distributions
- Re-Weighting Instances
- Learning Transformations
- Deep Domain Adaptation
- Using Deep Features in Traditional DA
- Aligning Data Representations
- Aligning Data Distributions
- Marginal Feature Distribution Alignment
- Class-Conditional Distribution Alignment
- Network Parameter Adaptation
- Parameter Fine-Tuning
- Domain Specific Batch Normalization
- Domain-Specific Network Weights
- Pixel Level Domain Style Transfer
- Self-Based Learning for DA
- Self-Training
- Pseudo-Labels for Adaptive Learning
- Labeling Confidence and Curriculum Learning
- Entropy Minimization
- Self-Ensembling
- Self-Supervision and Test Time Training
- Beyond Classical Domain Adaptation
- Multi-Source Domain Adaptation
- Shallow MSDA
- Deep MSDA
- Multi-Target Domain Adaptation
- Moving Toward Domain Generalization
- Reducing Source Knowledge
- Class Label Mismatch Across Domains
- Partial DA
- Open Set DA
- Universal DA
- Conclusions and More Settings
- Domain Generalization
- Architectural Approaches to DG
- Regularisation Approaches to DG
- Data Augmentation Approaches to DG
- Discussion and Outlook
- Learning to Learn Across Domains
- Learning-to-Learn Preliminaries
- Meta-Learning for Domain Adaptation
- Meta-Learning for Multi-Source UDA
- Discussion
- Meta-Learning for DG
- Multi-Source Domain Generalization
- Meta-Representations for DG
- Discussion
- Cross-Domain Few-Shot Learning
- Cross-Domain Few-Shot Learning Objective
- Meta-Representations for CD-FSL
- Discussion
- Conclusion
- Bibliography
- Authors' Biographies
System requirements
File format: PDF
Copy protection: Watermark-DRM (Digital Rights Management)
System requirements:
- Computer (Windows; MacOS X; Linux): Use the free software Adobe Reader, Adobe Digital Editions, or any other PDF viewer of your choice (see eBook Help).
- Tablet/Smartphone (Android; iOS): Install the free app Adobe Digital Editions or another reading app for eBooks, e.g., PocketBook (see eBook Help).
- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (only limited: Kindle).
The file format PDF always displays a book page identically on any hardware. This makes PDF suitable for complex layouts such as those used in textbooks and reference books (images, tables, columns, footnotes). Unfortunately, on the small screens of e-readers or smartphones, PDFs are rather annoying, requiring too much scrolling.
This eBook uses Watermark-DRM, a „soft” copy protection. This means that there are no technical restrictions to prevent illegal distribution. However, there is a personalised watermark embedded in the eBook that can be used to identify the purchaser of the eBook in the event of misuse and to provide evidence for legal purposes.
For more information, see our eBook Help page.