Output Layer Multiplication for Class Imbalance Problem in Convolutional Neural Networks
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Dapeng Tao | Zhao Yang | Sai Zhao | Tie Liu | Yuanxin Zhu | Yunyan Wang | Dapeng Tao | Yuanxin Zhu | Yunyan Wang | Zhao Yang | Sai Zhao | Tie Liu
[1] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[2] Huajun Feng,et al. Libra R-CNN: Towards Balanced Learning for Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Kay Chen Tan,et al. Evolutionary Cluster-Based Synthetic Oversampling Ensemble (ECO-Ensemble) for Imbalance Learning , 2017, IEEE Transactions on Cybernetics.
[4] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[5] David Masko,et al. The Impact of Imbalanced Training Data for Convolutional Neural Networks , 2015 .
[6] Shan Li,et al. Real world expression recognition: A highly imbalanced detection problem , 2016, 2016 International Conference on Biometrics (ICB).
[7] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[8] Robert C. Holte,et al. C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling , 2003 .
[9] Qi Tian,et al. Scalable Person Re-identification: A Benchmark , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[10] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[11] Kay Chen Tan,et al. Training cost-sensitive Deep Belief Networks on imbalance data problems , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[12] Abhinav Gupta,et al. A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Mantao Xu,et al. Classification of Imbalanced Data by Using the SMOTE Algorithm and Locally Linear Embedding , 2006, 2006 8th international Conference on Signal Processing.
[15] Wei Liu,et al. SSD: Single Shot MultiBox Detector , 2015, ECCV.
[16] Sinan Kalkan,et al. Imbalance Problems in Object Detection: A Review , 2020, IEEE transactions on pattern analysis and machine intelligence.
[17] Wei Lin,et al. Learning From Synthetic Data for Crowd Counting in the Wild , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Ali Farhadi,et al. You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[20] François Fleuret,et al. Not All Samples Are Created Equal: Deep Learning with Importance Sampling , 2018, ICML.
[21] Shaogang Gong,et al. Imbalanced Deep Learning by Minority Class Incremental Rectification , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[22] Xin Yao,et al. Diversity analysis on imbalanced data sets by using ensemble models , 2009, 2009 IEEE Symposium on Computational Intelligence and Data Mining.
[23] Yue-Shi Lee,et al. Cluster-based under-sampling approaches for imbalanced data distributions , 2009, Expert Syst. Appl..
[24] Bo Du,et al. Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding , 2015, Pattern Recognit..
[25] Kaiming He,et al. Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[26] Rong Chen,et al. Improved SMOTE Algorithm to Deal with Imbalanced Activity Classes in Smart Homes , 2018, Neural Processing Letters.
[27] Kaizhu Huang,et al. Learning Imbalanced Classifiers Locally and Globally with One-Side Probability Machine , 2014, Neural Processing Letters.
[28] Atsuto Maki,et al. A systematic study of the class imbalance problem in convolutional neural networks , 2017, Neural Networks.
[29] Chen Huang,et al. Learning Deep Representation for Imbalanced Classification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Eyad Elyan,et al. Neighbourhood-based undersampling approach for handling imbalanced and overlapped data , 2020, Inf. Sci..
[31] Colin Wei,et al. Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss , 2019, NeurIPS.
[32] Fei Su,et al. Deep class-skewed learning for face recognition , 2019, Neurocomputing.
[33] Taghi M. Khoshgoftaar,et al. RUSBoost: A Hybrid Approach to Alleviating Class Imbalance , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.
[34] Haibo He,et al. Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.
[35] Nitesh V. Chawla,et al. SMOTEBoost: Improving Prediction of the Minority Class in Boosting , 2003, PKDD.
[36] Bin Yang,et al. Learning to Reweight Examples for Robust Deep Learning , 2018, ICML.
[37] Charles Elkan,et al. The Foundations of Cost-Sensitive Learning , 2001, IJCAI.
[38] Vasile Palade,et al. Efficient resampling methods for training support vector machines with imbalanced datasets , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).
[39] James M. Rehg,et al. Learning to Generate Synthetic Data via Compositing , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[40] Xuelong Li,et al. Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes , 2019, IEEE Transactions on Image Processing.
[41] Yuri Sousa Aurelio,et al. Learning from Imbalanced Data Sets with Weighted Cross-Entropy Function , 2019, Neural Processing Letters.
[42] Francisco Herrera,et al. A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[43] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[44] Shree K. Nayar,et al. Ieee Transactions on Pattern Analysis and Machine Intelligence Describable Visual Attributes for Face Verification and Image Search , 2022 .
[45] Mohammed Bennamoun,et al. Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data , 2015, IEEE Transactions on Neural Networks and Learning Systems.
[46] Roberto Alejo,et al. An Efficient Over-sampling Approach Based on Mean Square Error Back-propagation for Dealing with the Multi-class Imbalance Problem , 2014, Neural Processing Letters.
[47] Chen Huang,et al. Deep Imbalanced Learning for Face Recognition and Attribute Prediction , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[48] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[49] Yang Song,et al. Class-Balanced Loss Based on Effective Number of Samples , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[50] Rosa Maria Valdovinos,et al. New Applications of Ensembles of Classifiers , 2003, Pattern Analysis & Applications.
[51] Iasonas Kokkinos,et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.