Domain Adaptation in Computer Vision Applications, Softcover reprint of the original 1st ed. 2017 Advances in Computer Vision and Pattern Recognition Series
Coordonnateur : Csurka Gabriela
This authoritative volume will be of great interest to a broad audience ranging from researchers and practitioners, to students involved in computer vision, pattern recognition and machine learning.
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.
Date de parution : 05-2018
Ouvrage de 344 p.
15.5x23.5 cm
Date de parution : 10-2017
Ouvrage de 344 p.
15.5x23.5 cm
Thème de Domain Adaptation in Computer Vision Applications :
Mots-clés :
Computer Vision; Visual Applications; Image Categorization; Pattern Recognition; Data Analytics; Unsupervised Domain Adaptation; Transductive Transfer Learning; Domain Shift; Feature Transformation; Subspace Learning; Landmark Selection; Maximum Mean Discrepancy; Grassman Manifold; Geodesic Flow; Subspace Alignment; Marginalized Denoising Autoencoders; Deep Learning; Domain-Adversarial Training