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![• Many real-world datasets have imbalanced class distributions
• Standard training (e.g. ERM) often fails to generalize at the “tail” classes
[Wang et al. 2017; Cui et al. 2019; Cao et al. 2019]
Class Imbalance in Training Data
Species [Van Horn et al. 2019] Places [Wang et al. 2017]
[Wang et al. 2017] Learning to Model the Tail. NeurIPS, 2017
[Van Horn et al. 2019] The iNaturalist Species Classification and Detection Dataset. In CVPR, 2018
[Cao et al. 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. In NeurIPS 2019
[Cui et al. 2019] Class-balanced Loss Based on Effective Number of Samples. In CVPR, 2019](https://image.slidesharecdn.com/m2m-200514053425/75/M2m-Imbalanced-Classification-via-Major-to-minor-Translation-CVPR-2020-2-2048.jpg)
![• Many real-world datasets have imbalanced class distributions
• Standard training (e.g. ERM) often fails to generalize at the “tail” classes
[Wang et al. 2017; Cui et al. 2019; Cao et al. 2019]
• Several training strategies have been investigated
Class Imbalance in Training Data
Re-balancing methods
• Re-sampling
[Japkowicz et al. 2000; Chawla et al. 2002]
• Re-weighting
[Khan et al. 2017; Cui et al. 2019]
Regularization methods
• Margin-based method
[Dong et al. 2017; Cao et al. 2019]
• Minority focused loss
[Lin et al. 2017]
Fundamental problem: Limited information of minority classes
[Japkowicz et al. 2000] The Class Imbalance Problem: Significance and Strategies. In ICAI 2000
[Chawla et al. 2002] SMOTE: Synthetic Minority Oversampling Technique. In JAIR 2002
[Khan et al. 2017] Cost-sensitive Learning of Deep Feature Representations from Imbalanced Data. In TNNLS 2017
[Dong et al. 2018] Imbalanced Deep Learning by Minority Class Incremental Rectification., TPAMI 2018
[Lin et al. 2017] Focal Loss for Dense Object Detection., CVPR 2017](https://image.slidesharecdn.com/m2m-200514053425/75/M2m-Imbalanced-Classification-via-Major-to-minor-Translation-CVPR-2020-3-2048.jpg)
![• Two evaluation metrics for imbalanced classification models:
• Balanced accuracy (bACC): arithmetic mean over class-wise sensitivity
• Geometric mean scores (GM): geometric mean over class-wise sensitivity
• Various types of baseline methods
• Re-sampling (RS) and re-weighting (RW)
• Re-sampling variants: SMOTE, deferred re-sampling (DRS)
[Chawla et al. 2002; Cao et al. 2019]
• Re-weighting variants: class-balanced RW (CB-RW) [Cui et al. 2019]
• Minority regularization: focal loss (Focal), label-dist. aware margin (LDAM)
[Lin et al. 2017; Cao et al. 2019]
Experiments
[Chawla et al. 2002] SMOTE: Synthetic Minority Oversampling Technique. In JAIR 2002
[Lin et al. 2017] Focal Loss for Dense Object Detection., CVPR 2017
[Cao et al. 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. In NeurIPS 2019
[Cui et al. 2019] Class-balanced Loss Based on Effective Number of Samples. In CVPR, 2019](https://image.slidesharecdn.com/m2m-200514053425/75/M2m-Imbalanced-Classification-via-Major-to-minor-Translation-CVPR-2020-7-2048.jpg)
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M2m: Imbalanced Classification via Major-to-minor Translation (CVPR 2020)
- 1. M2m: Imbalanced Classificationvia Major-to-minor Translation Jaehyung Kim* Jongheon Jeong* Jinwoo Shin *Equal contribution Korea Advanced Institute of Science and Technology (KAIST)
- 2. • Many real-worlddatasets have imbalanced class distributions • Standard training (e.g. ERM) often fails to generalize at the “tail” classes [Wang et al. 2017; Cui et al. 2019; Cao et al. 2019] Class Imbalance in Training Data Species [Van Horn et al. 2019] Places [Wang et al. 2017] [Wang et al. 2017] Learning to Model the Tail. NeurIPS, 2017 [Van Horn et al. 2019] The iNaturalist Species Classification and Detection Dataset. In CVPR, 2018 [Cao et al. 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. In NeurIPS 2019 [Cui et al. 2019] Class-balanced Loss Based on Effective Number of Samples. In CVPR, 2019
- 3. • Many real-worlddatasets have imbalanced class distributions • Standard training (e.g. ERM) often fails to generalize at the “tail” classes [Wang et al. 2017; Cui et al. 2019; Cao et al. 2019] • Several training strategies have been investigated Class Imbalance in Training Data Re-balancing methods • Re-sampling [Japkowicz et al. 2000; Chawla et al. 2002] • Re-weighting [Khan et al. 2017; Cui et al. 2019] Regularization methods • Margin-based method [Dong et al. 2017; Cao et al. 2019] • Minority focused loss [Lin et al. 2017] Fundamental problem: Limited information of minority classes [Japkowicz et al. 2000] The Class Imbalance Problem: Significance and Strategies. In ICAI 2000 [Chawla et al. 2002] SMOTE: Synthetic Minority Oversampling Technique. In JAIR 2002 [Khan et al. 2017] Cost-sensitive Learning of Deep Feature Representations from Imbalanced Data. In TNNLS 2017 [Dong et al. 2018] Imbalanced Deep Learning by Minority Class Incremental Rectification., TPAMI 2018 [Lin et al. 2017] Focal Loss for Dense Object Detection., CVPR 2017
- 4. • M2m augmentsminority classes using information of majority samples • Over-fitting on minority classes is prevented by utilizing the diversity of majority • A simple optimization using a pre-trained classifier surprisingly works well M2m: Major-to-minor Translation
- 5. • M2m augmentsminority classes via translating from majority samples • Each translation is done by solving the following optimization: M2m: Major-to-minor Translation Optimization objective of M2m (a) Translation to minority class - Using a pre-trained classifier (b) Regularization for reducing a risk as majority class - On the logit of training classifier is used as a minority sample for training f<latexit sha1_base64="dC5uJX4s+XLUhctJUEqp/+YNQhw=">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</latexit><latexit 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- 6. • Rejection criterion:selective use of translated sample • This reduces a risk of unreliable generation when is small • Optimal sampling : better choice of majority seed • Majority classes to sample are selected by considering two aspects: (a) Maximizes the acceptance probability (b) Chooses diverse classes M2m: Techniques for Better Efficiency x⇤ <latexit sha1_base64="F1KMxJXvEGNv3MpOrIRy7Tp7EXs=">AAAB7HicbVA9SwNBEJ2LXzF+RS1tFoMgFuFOBC0sAjaWEbwkkJxhbzNJluztHbt7YjjyG2wsFLH1B9n5b9wkV2jig4HHezPMzAsTwbVx3W+nsLK6tr5R3Cxtbe/s7pX3Dxo6ThVDn8UiVq2QahRcom+4EdhKFNIoFNgMRzdTv/mISvNY3ptxgkFEB5L3OaPGSv7TQ3Y26ZYrbtWdgSwTLycVyFHvlr86vZilEUrDBNW67bmJCTKqDGcCJ6VOqjGhbEQH2LZU0gh1kM2OnZATq/RIP1a2pCEz9fdERiOtx1FoOyNqhnrRm4r/ee3U9K+CjMskNSjZfFE/FcTEZPo56XGFzIixJZQpbm8lbEgVZcbmU7IheIsvL5PGedVzq97dRaV2ncdRhCM4hlPw4BJqcAt18IEBh2d4hTdHOi/Ou/Mxby04+cwh/IHz+QPCTo6e</latexit><latexit 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sha1_base64="5huwj3zFVPbko7iCRKaz92eG43g=">AAAB7HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0IOHghePFUxbaEPZbCft0s0m7G7EEvobvHhQxKs/yJv/xm2bg7Y+GHi8N8PMvDAVXBvX/XZKa+sbm1vl7crO7t7+QfXwqKWTTDH0WSIS1QmpRsEl+oYbgZ1UIY1Dge1wfDvz24+oNE/kg5mkGMR0KHnEGTVW8p/6uTvtV2tu3Z2DrBKvIDUo0OxXv3qDhGUxSsME1brruakJcqoMZwKnlV6mMaVsTIfYtVTSGHWQz4+dkjOrDEiUKFvSkLn6eyKnsdaTOLSdMTUjvezNxP+8bmai6yDnMs0MSrZYFGWCmITMPicDrpAZMbGEMsXtrYSNqKLM2HwqNgRv+eVV0rqoe27du7+sNW6KOMpwAqdwDh5cQQPuoAk+MODwDK/w5kjnxXl3PhatJaeYOYY/cD5/AMzzjqU=</latexit> # samples of class Nk0<latexit 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- 7. • Two evaluationmetrics for imbalanced classification models: • Balanced accuracy (bACC): arithmetic mean over class-wise sensitivity • Geometric mean scores (GM): geometric mean over class-wise sensitivity • Various types of baseline methods • Re-sampling (RS) and re-weighting (RW) • Re-sampling variants: SMOTE, deferred re-sampling (DRS) [Chawla et al. 2002; Cao et al. 2019] • Re-weighting variants: class-balanced RW (CB-RW) [Cui et al. 2019] • Minority regularization: focal loss (Focal), label-dist. aware margin (LDAM) [Lin et al. 2017; Cao et al. 2019] Experiments [Chawla et al. 2002] SMOTE: Synthetic Minority Oversampling Technique. In JAIR 2002 [Lin et al. 2017] Focal Loss for Dense Object Detection., CVPR 2017 [Cao et al. 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. In NeurIPS 2019 [Cui et al. 2019] Class-balanced Loss Based on Effective Number of Samples. In CVPR, 2019
- 8. • Synthetically imbalancedversion of CIFAR-10/100 • We control the imbalance ratio • are set to follow an exponential decay across Experiments: Long-tailed CIFAR-10/100 ⇢ = N1/Nk<latexit sha1_base64="NwvRw269Yl0ESgPSsPYYzPTnY3I=">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</latexit><latexit 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- 9. • Datasets withnatural imbalances • Vision tasks: CelebA-5 and SUN397 • NLP tasks: Twitter and Reuters • Tend to have a much significant imbalance Experiments: Real-world Imbalanced Datasets M2m works even better under a harsh imbalance
- 10. • A simpleyet powerful over-sampling for imbalanced classification • Diversity of majority is effective to overcome the scarcity of minority • Adversarial examples could be a good feature at least for the minority Conclusion Thank you for your attention 😄