A Tale of Two CILs: The Connections between Class Incremental Learning and Class Imbalanced Learning, and Beyond
暂无分享,去创建一个
Xilin Chen | Ruiping Wang | Chen He | Ruiping Wang | Xilin Chen | Chen He
[1] Bianca Zadrozny,et al. Transforming classifier scores into accurate multiclass probability estimates , 2002, KDD.
[2] Terrance E. Boult,et al. Towards Open World Recognition , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Yang Song,et al. The iNaturalist Species Classification and Detection Dataset , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[4] Wouter M. Kouw. An introduction to domain adaptation and transfer learning , 2018, ArXiv.
[5] Michael McCloskey,et al. Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem , 1989 .
[6] Zhi-Hua Zhou,et al. Ieee Transactions on Knowledge and Data Engineering 1 Training Cost-sensitive Neural Networks with Methods Addressing the Class Imbalance Problem , 2022 .
[7] Yandong Guo,et al. Large Scale Incremental Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Patrick Jähnichen,et al. Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Yandong Guo,et al. Incremental Classifier Learning with Generative Adversarial Networks , 2018, ArXiv.
[10] Seetha Hari,et al. Learning From Imbalanced Data , 2019, Advances in Computer and Electrical Engineering.
[11] Ling Shao,et al. Gaussian Affinity for Max-Margin Class Imbalanced Learning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[12] Wei Liu,et al. Class Confidence Weighted kNN Algorithms for Imbalanced Data Sets , 2011, PAKDD.
[13] Ross B. Girshick,et al. LVIS: A Dataset for Large Vocabulary Instance Segmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Richard Lippmann,et al. Neural Network Classifiers Estimate Bayesian a posteriori Probabilities , 1991, Neural Computation.
[15] Ryota Tomioka,et al. Norm-Based Capacity Control in Neural Networks , 2015, COLT.
[16] Nikos Komodakis,et al. Dynamic Few-Shot Visual Learning Without Forgetting , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[17] Jinhui Tang,et al. Few-Shot Image Recognition With Knowledge Transfer , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[18] 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).
[19] J. Hintze,et al. Violin plots : A box plot-density trace synergism , 1998 .
[20] Teuvo Kohonen,et al. The self-organizing map , 1990, Neurocomputing.
[21] Mark Sandler,et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[22] Dragomir Anguelov,et al. Capturing Long-Tail Distributions of Object Subcategories , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[23] Charles X. Ling,et al. Data Mining for Direct Marketing: Problems and Solutions , 1998, KDD.
[24] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[25] Francisco Herrera,et al. A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[26] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[27] Bianca Zadrozny,et al. Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers , 2001, ICML.
[28] Marcus Rohrbach,et al. Decoupling Representation and Classifier for Long-Tailed Recognition , 2020, ICLR.
[29] Philip H. S. Torr,et al. GDumb: A Simple Approach that Questions Our Progress in Continual Learning , 2020, ECCV.
[30] Fengqing Zhu,et al. Incremental Learning in Online Scenario , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Frank Hutter,et al. A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets , 2017, ArXiv.
[32] Qingming Huang,et al. Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks , 2015, ECCV.
[33] Atsuto Maki,et al. A systematic study of the class imbalance problem in convolutional neural networks , 2017, Neural Networks.
[34] Fernando Nogueira,et al. Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning , 2016, J. Mach. Learn. Res..
[35] Haibo He,et al. ADASYN: Adaptive synthetic sampling approach for imbalanced learning , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).
[36] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[37] R. Solomonoff. A SYSTEM FOR INCREMENTAL LEARNING BASED ON ALGORITHMIC PROBABILITY , 1989 .
[38] Lei Zhang,et al. One-shot Face Recognition by Promoting Underrepresented Classes , 2017, ArXiv.
[39] Yen-Cheng Liu,et al. Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines , 2018, ArXiv.
[40] Peter A. Flach,et al. Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration , 2019, NeurIPS.
[41] Tao Lin,et al. Generalized Class Incremental Learning , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[42] Derek Hoiem,et al. Learning without Forgetting , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[43] N. Japkowicz. Learning from Imbalanced Data Sets: A Comparison of Various Strategies * , 2000 .
[44] Christopher Joseph Pal,et al. Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..
[45] Francisco Herrera,et al. A unifying view on dataset shift in classification , 2012, Pattern Recognit..
[46] Seong Joon Oh,et al. CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[47] Ming-Hsuan Yang,et al. Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition From a Domain Adaptation Perspective , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[48] Gaël Varoquaux,et al. The NumPy Array: A Structure for Efficient Numerical Computation , 2011, Computing in Science & Engineering.
[49] Nathan Srebro,et al. Exploring Generalization in Deep Learning , 2017, NIPS.
[50] Christoph H. Lampert,et al. iCaRL: Incremental Classifier and Representation Learning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[51] Nathalie Japkowicz,et al. The class imbalance problem: A systematic study , 2002, Intell. Data Anal..
[52] Carla E. Brodley,et al. Class Imbalance, Redux , 2011, 2011 IEEE 11th International Conference on Data Mining.
[53] Fei Yin,et al. Robust Classification with Convolutional Prototype Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[54] Frank Hutter,et al. SGDR: Stochastic Gradient Descent with Warm Restarts , 2016, ICLR.
[55] Adrian Popescu,et al. IL2M: Class Incremental Learning With Dual Memory , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[56] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[57] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[58] Ling Shao,et al. Striking the Right Balance With Uncertainty , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[59] Shutao Xia,et al. Maintaining Discrimination and Fairness in Class Incremental Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[60] Yuan Yu,et al. TensorFlow: A system for large-scale machine learning , 2016, OSDI.
[61] Faisal Shafait,et al. Revisiting Distillation and Incremental Classifier Learning , 2018, ACCV.
[62] Cordelia Schmid,et al. End-to-End Incremental Learning , 2018, ECCV.
[63] Jiwon Kim,et al. Continual Learning with Deep Generative Replay , 2017, NIPS.
[64] Philip H. S. Torr,et al. Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence , 2018, ECCV.
[65] Peter A. Flach,et al. Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration , 2017 .
[66] Shiguang Shan,et al. Exemplar-Supported Generative Reproduction for Class Incremental Learning , 2018, BMVC.
[67] Shih-Fu Chang,et al. Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks , 2018, NeurIPS.
[68] Barbara Hammer,et al. Incremental learning algorithms and applications , 2016, ESANN.
[69] Xiu-Shen Wei,et al. BBN: Bilateral-Branch Network With Cumulative Learning for Long-Tailed Visual Recognition , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[70] Andreas S. Tolias,et al. Three scenarios for continual learning , 2019, ArXiv.
[71] Kilian Q. Weinberger,et al. On Calibration of Modern Neural Networks , 2017, ICML.
[72] Shigeo Abe DrEng. Pattern Classification , 2001, Springer London.
[73] Ruiping Wang,et al. CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions , 2020, Artif. Intell..
[74] Ah Chung Tsoi,et al. Neural Network Classification and Prior Class Probabilities , 1996, Neural Networks: Tricks of the Trade.