Domain Adaptation for Visual Applications: A Comprehensive Survey
arXiv:1702.05374
Abstract
The aim of this paper is to give an overview of domain adaptation and transfer learning with a specific view on visual applications. After a general motivation, we first position domain adaptation in the larger transfer learning problem. Second, we try to address and analyze briefly the state-of-the-art methods for different types of scenarios, first describing the historical shallow methods, addressing both the homogeneous and the heterogeneous domain adaptation methods. Third, we discuss the effect of the success of deep convolutional architectures which led to new type of domain adaptation methods that integrate the adaptation within the deep architecture. Fourth, we overview the methods that go beyond image categorization, such as object detection or image segmentation, video analyses or learning visual attributes. Finally, we conclude the paper with a section where we relate domain adaptation to other machine learning solutions.
Book chapter to appear in "Domain Adaptation in Computer Vision Applications", Springer Series: Advances in Computer Vision and Pattern Recognition, Edited by Gabriela Csurka
References in corpus (11)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Deep Domain Confusion: Maximizing for Domain Invariance
- Frustratingly Easy Domain Adaptation
- A Survey on Multi-view Learning
- Adversarial Discriminative Domain Adaptation
- Deep CORAL: Correlation Alignment for Deep Domain Adaptation
- Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition
- Playing for Data: Ground Truth from Computer Games
- On Rendering Synthetic Images for Training an Object Detector
- Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
- Self-Learning Camera: Autonomous Adaptation of Object Detectors to Unlabeled Video Streams