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Human attribute analysis is a challenging task in the field of computer vision, since the data is largely imbalance-distributed.
A comparative study of cost-sensitive boosting algorithms
Kai Ming Ting · 2000
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Smote: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer · 2002
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Smoteboost: Improving prediction of the minority class in boosting
Nitesh V Chawla, Aleksandar Lazarevic, Lawrence O Hall, and Kevin W Bowyer · 2003
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C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling
Chris Drummond, Robert C Holte, et al · 2003
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Cost-sensitive learning by cost-proportionate example weighting
Bianca Zadrozny, John Langford, and Naoki Abe · 2003
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Special issue on learning from imbalanced data sets
Nitesh V Chawla, Nathalie Japkowicz, and Aleksander Kotcz · 2004
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A multiple resampling method for learning from imbalanced data sets
Andrew Estabrooks, Taeho Jo, and Nathalie Japkowicz · 2004
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Borderline-smote: a new over-sampling method in imbalanced data sets learning
Hui Han, Wen-Yuan Wang, and Bing-Huan Mao · 2005
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Decision threshold adjustment in class prediction
JJ Chen, C-A Tsai, H Moon, H Ahn, JJ Young, and C-H Chen · 2006
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Training cost-sensitive neural networks with methods addressing the class imbalance problem
Zhi-Hua Zhou and Xu-Ying Liu · 2006
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Adasyn: Adaptive synthetic sampling approach for imbalanced learning
Haibo He, Yang Bai, Edwardo A Garcia, and Shutao Li · 2008
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Learning from imbalanced data
Haibo He and Edwardo A Garcia · 2008
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Exploratory undersampling for class-imbalance learning
Xu-Ying Liu, Jianxin Wu, and Zhi-Hua Zhou · 2009
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Svms modeling for highly imbalanced classification
Yuchun Tang, Yan-Qing Zhang, Nitesh V Chawla, and Sven Krasser · 2009
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Knowledge discovery from imbalanced and noisy data
Jason Van Hulse and Taghi Khoshgoftaar · 2009
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Margin calibration in svm class-imbalanced learning
Chan-Yun Yang, Jr-Syu Yang, and Jian-Jun Wang · 2009
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Classification of imbalanced data by combining the complementary neural network and smote algorithm
Piyasak Jeatrakul, Kok Wai Wong, and Chun Che Fung · 2010
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Supervised neural network modeling: an empirical investigation into learning from imbalanced data with labeling errors
Taghi M Khoshgoftaar, Jason Van Hulse, and Amri Napolitano · 2010
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Learning from imbalanced data in presence of noisy and borderline examples
Krystyna Napierała, Jerzy Stefanowski, and Szymon Wilk · 2010
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Cost-sensitive learning methods for imbalanced data
Nguyen Thai-Nghe, Zeno Gantner, and Lars Schmidt-Thieme · 2010
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Comparing boosting and bagging techniques with noisy and imbalanced data
Taghi M Khoshgoftaar, Jason Van Hulse, and Amri Napolitano · 2011
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Learning the easy things first: Self-paced visual category discovery
Yong Jae Lee and Kristen Grauman · 2011
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Local neighbourhood extension of smote for mining imbalanced data
Active learning with imbalanced multiple noisy labeling
Jing Zhang, Xindong Wu, and Victor S Shengs · 2015
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Multi-modal curriculum learning for semi-supervised image classification
Chen Gong, Dacheng Tao, Stephen J Maybank, Wei Liu, Guoliang Kang, and Jie Yang · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning deep representation for imbalanced classification
Chen Huang, Yining Li, Chen Change Loy, and Xiaoou Tang · 2016
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A richly annotated dataset for pedestrian attribute recognition
Dangwei Li, Zhang Zhang, Xiaotang Chen, Haibin Ling, and Kaiqi Huang · 2016
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Tomasz Maciejewski and Jerzy Stefanowski · 2011
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Imbalanced learning: foundations, algorithms, and applications
Haibo He and Yunqian Ma · 2013
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Graph classification with imbalanced class distributions and noise
Shirui Pan and Xingquan Zhu · 2013
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Self-paced learning for long-term tracking
James S Supancic and Deva Ramanan · 2013
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Learning and transferring mid-level image representations using convolutional neural networks
Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic · 2014
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An empirical study of the classification performance of learners on imbalanced and noisy software quality data
Chris Seiffert, Taghi M Khoshgoftaar, Jason Van Hulse, and Andres Folleco · 2014
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Hualong Yu, Changyin Sun, Xibei Yang, Wankou Yang, Jifeng Shen, and Yunsong Qi · 2016
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Class rectification hard mining for imbalanced deep learning
Qi Dong, Shaogang Gong, and Xiatian Zhu · 2017
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Hydraplus-net: Attentive deep features for pedestrian analysis
Xihui Liu, Haiyu Zhao, Maoqing Tian, Lu Sheng, Jing Shao, Shuai Yi, Junjie Yan, and Xiaogang Wang · 2017
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Deep view-sensitive pedestrian attribute inference in an end-to-end model
M Saquib Sarfraz, Arne Schumann, Yan Wang, and Rainer Stiefelhagen · 2017
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Attribute recognition by joint recurrent learning of context and correlation
Jingya Wang, Xiatian Zhu, Shaogang Gong, and Wei Li · 2017
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Imbalanced deep learning by minority class incremental rectification
Qi Dong, Shaogang Gong, and Xiatian Zhu · 2018
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Curriculumnet: Weakly supervised learning from large-scale web images
Sheng Guo, Weilin Huang, Haozhi Zhang, Chenfan Zhuang, Dengke Dong, Matthew R Scott, and Dinglong Huang · 2018
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Doing the best we can with what we have: Multi-label balancing with selective learning for attribute prediction
Emily M Hand, Carlos D Castillo, and Rama Chellappa · 2018
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Deep imbalanced learning for face recognition and attribute prediction
Chen Huang, Yining Li, Chen Change Loy, and Xiaoou Tang · 2018
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Cost-sensitive learning of deep feature representations from imbalanced data
Salman H Khan, Munawar Hayat, Mohammed Bennamoun, Ferdous A Sohel, and Roberto Togneri · 2018
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Localization guided learning for pedestrian attribute recognition
Pengze Liu, Xihui Liu, Junjie Yan, and Jing Shao · 2018
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Curriculum learning of visual attribute clusters for multi-task classification
Nikolaos Sarafianos, Theodoros Giannakopoulos, Christophoros Nikou, and Ioannis A Kakadiaris · 2018
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Deep imbalanced attribute classification using visual attention aggregation
Nikolaos Sarafianos, Xiang Xu, and Ioannis A Kakadiaris · 2018
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Class-balanced loss based on effective number of samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie · 2019
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