Jaehyung Kim*, Jongheon Jeong*, Jinwoo Shin arXiv: https://arxiv.org/abs/2004.00431 code: https://github.com/alinlab/M2m

1. M2m: Imbalanced Classification via
Major-to-minor Translation
Jaehyung Kim* Jongheon Jeong* Jinwoo Shin
*Equal contribution
Korea Advanced Institute of Science and Technology (KAIST)

2. • 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

3. • 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

4. • M2m augments minority 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 augments minority 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
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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
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x0<latexit sha1_base64="5huwj3zFVPbko7iCRKaz92eG43g=">AAAB7HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0IOHghePFUxbaEPZbCft0s0m7G7EEvobvHhQxKs/yJv/xm2bg7Y+GHi8N8PMvDAVXBvX/XZKa+sbm1vl7crO7t7+QfXwqKWTTDH0WSIS1QmpRsEl+oYbgZ1UIY1Dge1wfDvz24+oNE/kg5mkGMR0KHnEGTVW8p/6uTvtV2tu3Z2DrBKvIDUo0OxXv3qDhGUxSsME1brruakJcqoMZwKnlV6mMaVsTIfYtVTSGHWQz4+dkjOrDEiUKFvSkLn6eyKnsdaTOLSdMTUjvezNxP+8bmai6yDnMs0MSrZYFGWCmITMPicDrpAZMbGEMsXtrYSNqKLM2HwqNgRv+eVV0rqoe27du7+sNW6KOMpwAqdwDh5cQQPuoAk+MODwDK/w5kjnxXl3PhatJaeYOYY/cD5/AMzzjqU=</latexit><latexit sha1_base64="5huwj3zFVPbko7iCRKaz92eG43g=">AAAB7HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0IOHghePFUxbaEPZbCft0s0m7G7EEvobvHhQxKs/yJv/xm2bg7Y+GHi8N8PMvDAVXBvX/XZKa+sbm1vl7crO7t7+QfXwqKWTTDH0WSIS1QmpRsEl+oYbgZ1UIY1Dge1wfDvz24+oNE/kg5mkGMR0KHnEGTVW8p/6uTvtV2tu3Z2DrBKvIDUo0OxXv3qDhGUxSsME1brruakJcqoMZwKnlV6mMaVsTIfYtVTSGHWQz4+dkjOrDEiUKFvSkLn6eyKnsdaTOLSdMTUjvezNxP+8bmai6yDnMs0MSrZYFGWCmITMPicDrpAZMbGEMsXtrYSNqKLM2HwqNgRv+eVV0rqoe27du7+sNW6KOMpwAqdwDh5cQQPuoAk+MODwDK/w5kjnxXl3PhatJaeYOYY/cD5/AMzzjqU=</latexit><latexit sha1_base64="5huwj3zFVPbko7iCRKaz92eG43g=">AAAB7HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0IOHghePFUxbaEPZbCft0s0m7G7EEvobvHhQxKs/yJv/xm2bg7Y+GHi8N8PMvDAVXBvX/XZKa+sbm1vl7crO7t7+QfXwqKWTTDH0WSIS1QmpRsEl+oYbgZ1UIY1Dge1wfDvz24+oNE/kg5mkGMR0KHnEGTVW8p/6uTvtV2tu3Z2DrBKvIDUo0OxXv3qDhGUxSsME1brruakJcqoMZwKnlV6mMaVsTIfYtVTSGHWQz4+dkjOrDEiUKFvSkLn6eyKnsdaTOLSdMTUjvezNxP+8bmai6yDnMs0MSrZYFGWCmITMPicDrpAZMbGEMsXtrYSNqKLM2HwqNgRv+eVV0rqoe27du7+sNW6KOMpwAqdwDh5cQQPuoAk+MODwDK/w5kjnxXl3PhatJaeYOYY/cD5/AMzzjqU=</latexit><latexit 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
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major minor
Q(k0|k) / 1 (Nk0
Nk)+
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7. • 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

8. • Synthetically imbalanced version of CIFAR-10/100
• We control the imbalance ratio
• are set to follow an exponential decay across
Experiments: Long-tailed CIFAR-10/100
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M2m surpasses all the
baselines tested
M2m further improves
existing regularization
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9. • Datasets with natural 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 simple yet 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 😄