Domain Adaptation for Visual Recognition

2015 ◽  
Vol 8 (4) ◽  
pp. 285-378 ◽  
Author(s):  
Raghuraman Gopalan ◽  
Ruonan Li ◽  
Vishal M. Patel ◽  
Rama Chellappa
Keyword(s):  
Visual Recognition ◽  
Domain Adaptation

Domain Adaptation for Visual Recognition

2015 ◽  
Author(s):  
Raghuraman Gopalan ◽  
Ruonan Li ◽  
Vishal M. Patel ◽  
Rama Chellappa
Keyword(s):  
Visual Recognition ◽  
Domain Adaptation

Robust adversarial discriminative domain adaptation for real-world cross-domain visual recognition

2021 ◽  
Author(s):  
Jianfei Yang ◽  
Han Zou ◽  
Yuxun Zhou ◽  
Lihua Xie
Keyword(s):  
Real World ◽  
Visual Recognition ◽  
Domain Adaptation ◽  
Cross Domain

scholarly journals Semi-supervised Domain Adaptation with Subspace Learning for visual recognition

2015 ◽  
Author(s):  
Ting Yao ◽  
Yingwei Pan ◽  
Chong-Wah Ngo ◽  
Houqiang Li ◽  
Tao Mei
Keyword(s):  
Visual Recognition ◽  
Domain Adaptation ◽  
Subspace Learning

scholarly journals Domain adaptation and generalization for visual recognition

2017 ◽  
Author(s):  
◽  
Li Niu
Keyword(s):  
Visual Recognition ◽  
Domain Adaptation

scholarly journals Adaptive Inception Based on Transfer Learning for Effective Visual Recognition

2020 ◽  
Vol 13 (6) ◽  
pp. 1-10
Author(s):  
Balaji Sreenivasulu ◽  
◽  
Anjaneyulu Pasala ◽  
Gaikwad Vasanth ◽  
◽  
...  
Keyword(s):  
Transfer Learning ◽  
Visual Recognition ◽  
Data Augmentation ◽  
Research Study ◽  
Domain Adaptation ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Convolutional Networks ◽  
Learning Techniques ◽  
Two Phases

In computer vision, domain adaptation or transfer learning plays an important role because it learns a target classifier characteristics using labeled data from various distribution. The existing researches mostly focused on minimizing the time complexity of neural networks and it effectively worked on low-level features. However, the existing method failed to concentrate on data augmentation time and cost of labeled data. Moreover, machine learning techniques face difficulty to obtain the large amount of distributed label data. In this research study, the pre-trained network called inception layer is fine-tuned with the augmented data. There are two phases present in this study, where the effectiveness of data augmentation for Inception pre-trained networks is investigated in the first phase. The transfer learning approach is used to enhance the results of the first phase and the Support Vector Machine (SVM) is used to learn all the features extracted from inception layers. The experiments are conducted on a publicly available dataset to estimate the effectiveness of proposed method. The results stated that the proposed method achieved 95.23% accuracy, where the existing techniques namely deep neural network and traditional convolutional networks achieved 87.32% and 91.32% accuracy respectively. This validation results proved that the developed method nearly achieved 4-8% improvement in accuracy than existing techniques.

scholarly journals Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition From a Domain Adaptation Perspective

2020 ◽  
Author(s):  
Muhammad Abdullah Jamal ◽  
Matthew Brown ◽  
Ming-Hsuan Yang ◽  
Liqiang Wang ◽  
Boqing Gong
Keyword(s):  
Visual Recognition ◽  
Domain Adaptation

scholarly journals Unsupervised Domain Adaptation Using Exemplar-SVMs with Adaptation Regularization

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Yiwei He ◽  
Yingjie Tian ◽  
Jingjing Tang ◽  
Yue Ma
Keyword(s):  
Support Vector Machines ◽  
Selection Bias ◽  
Visual Recognition ◽  
Domain Adaptation ◽  
Sample Selection ◽  
Original Data ◽  
Sample Selection Bias ◽  
Support Vector ◽  
Target Domain ◽  
Vector Machines

Domain adaptation has recently attracted attention for visual recognition. It assumes that source and target domain data are drawn from the same feature space but different margin distributions and its motivation is to utilize the source domain instances to assist in training a robust classifier for target domain tasks. Previous studies always focus on reducing the distribution mismatch across domains. However, in many real-world applications, there also exist problems of sample selection bias among instances in a domain; this would reduce the generalization performance of learners. To address this issue, we propose a novel model named Domain Adaptation Exemplar Support Vector Machines (DAESVMs) based on exemplar support vector machines (exemplar-SVMs). Our approach aims to address the problems of sample selection bias and domain adaptation simultaneously. Comparing with usual domain adaptation problems, we go a step further in slacking the assumption of i.i.d. First, we formulate the DAESVMs training classifiers with reducing Maximum Mean Discrepancy (MMD) among domains by mapping data into a latent space and preserving properties of original data, and then, we integrate classifiers to make a prediction for target domain instances. Our experiments were conducted on Office and Caltech10 datasets and verify the effectiveness of the model we proposed.

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