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Distribution Aligning Refinery of Pseudo-label
for Imbalanced Semi-supervised Learning
Jaehyung Kim1 Youngbum Hur2 Sejun P...
• Goal: reduce the need for labeled data by leveraging unlabeled data
• Common approach for SSL: generating pseudo-labels ...
• Balanced class distribution is typically assumed in the existing works for SSL
• However, many real-world datasets have ...
• Assumption: class distribution of labeled and unlabeled data is highly imbalanced
Imbalanced Semi-supervised Learning
Im...
• Assumption: class distribution of labeled and unlabeled data is highly imbalanced
• Under imbalanced SSL scenario, recen...
• Idea: refining the original, biased pseudo-labels from SSL methods
• Distribution of refined pseudo-labels matches the t...
• For solving the optimization, we propose an efficient iterative algorithm
• It is a coordinate ascent algorithm for solv...
• Both labeled and unlabeled data have the same class distribution
• Number of unlabeled data is inferred from that of lab...
• Class distribution of unlabeled data is not same as that of labeled data
• Estimated number of unlabeled data is used fo...
• We investigate imbalanced SSL, which is an important but under-explored
• We identify that current SSL algorithms can be...
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Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning Slide 1 Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning Slide 2 Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning Slide 3 Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning Slide 4 Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning Slide 5 Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning Slide 6 Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning Slide 7 Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning Slide 8 Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning Slide 9 Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning Slide 10
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Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning

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Official slides for the NeurIPS 2020 paper "Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning," by Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang, Sung Ju Hwang, and Jinwoo Shin.

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Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning

  1. 1. Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning Jaehyung Kim1 Youngbum Hur2 Sejun Park1 Eunho Yang1,3 Sung Ju Hwang1,3 Jinwoo Shin1 1Korea Advanced Institute of Science and Technology (KAIST) 2Samsung Advanced Institute of Technology 3AITRICS
  2. 2. • Goal: reduce the need for labeled data by leveraging unlabeled data • Common approach for SSL: generating pseudo-labels for unlabeled data • Generating method and loss function are just different among them • For example, the prediction of augmented data has been used as pseudo-labels [Miyato et al., 2018; Berthelot et al., 2019; Sohn et al; 2020] Semi-supervised Learning (SSL) [Miyato et al. 2018] Virtual Adversarial Training: A Regularization Method for Supervised and Semi-supervised Learning. In PAMI, 2018 [Berthelot et al. 2019] MixMatch: A Holistic Approach to Semi-supervised Learning. In NeurIPS, 2019 [Sohn et al. 2020] Fixmatch: Simplifying Semi-sueprvised Learning with Consistency and Confidence. In NeurIPS, 2020 Generation method of pseudo-label in MixMatch [Berthelot et al. 2019] 1
  3. 3. • Balanced class distribution is typically assumed in the existing works for SSL • However, many real-world datasets have an imbalanced class distribution • Standard training (e.g. ERM) often fails to generalize at the minority classes [Wang et al., 2017; 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. In 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 2
  4. 4. • Assumption: class distribution of labeled and unlabeled data is highly imbalanced Imbalanced Semi-supervised Learning Imbalanced CIFAR-10 3
  5. 5. • Assumption: class distribution of labeled and unlabeled data is highly imbalanced • Under imbalanced SSL scenario, recent SSL methods do not work well • They generate pseudo-labels of unlabeled data from the model’s biased predictions • Pseudo-labels are even more severely imbalanced ⟹ degradation on minority classes Imbalanced Semi-supervised Learning 3 Results on imbalanced CIFAR-10
  6. 6. • Idea: refining the original, biased pseudo-labels from SSL methods • Distribution of refined pseudo-labels matches the true class distribution of unlabeled data • Simultaneously, refined pseudo-labels are constrained to be not too far from the original ones • Refined pseudo-labels are obtained by solving a convex optimization • : number of unlabeled data for class , : number of classes, • Weight to preserve more information of high-confident original pseudo-labels Distribution Aligning Refinery of Pseudo-label (DARP) wm := H ˆyunlabeled m 1 <latexit 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  7. 7. • For solving the optimization, we propose an efficient iterative algorithm • It is a coordinate ascent algorithm for solving its Lagrangian dual with a provable guarantee • Number of unlabeled data can be inferred or simply estimated Distribution Aligning Refinery of Pseudo-label (DARP) Simple matrix multiplication Solved by existing efficient solver 5 {Mk}K k=1<latexit sha1_base64="lFWluioy0kF5xBwAEpS4DtmWbOY=">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</latexit><latexit sha1_base64="lFWluioy0kF5xBwAEpS4DtmWbOY=">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</latexit><latexit sha1_base64="lFWluioy0kF5xBwAEpS4DtmWbOY=">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</latexit><latexit sha1_base64="lFWluioy0kF5xBwAEpS4DtmWbOY=">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</latexit> Inferred or estimated
  8. 8. • Both labeled and unlabeled data have the same class distribution • Number of unlabeled data is inferred from that of labeled data • SSL: semi-supervised learning (not consider imbalance), RB: re-balancing (not use unlabeled data) Experiments: “Same” Class Distributions DARP improves the accuracy of all the applied baselines Biased pseudo-labels degrade the performance 6*bACC / GM: arithmetic / geometric mean over class-wise accuracy*Larger ⟹ More severely imbalanced<latexit sha1_base64="4JXcQvmS1cZ5PKlmHsv0tSJYNSM=">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</latexit><latexit sha1_base64="4JXcQvmS1cZ5PKlmHsv0tSJYNSM=">AAACyXicjVHLSsNAFD2Nr1pfVZdugkVwVRIRdFlwI7ipYB/Qikym0xqbl8lErMWVP+BWf0z8A/0L74xTUIvohCRnzr3nzNx7vSTwM+k4rwVrZnZufqG4WFpaXlldK69vNLM4T7lo8DiI07bHMhH4kWhIXwainaSChV4gWt7wSMVbNyLN/Dg6k6NEnIdsEPl9nzNJVLM7YGHILsoVp+roZU8D14AKzKrH5Rd00UMMjhwhBCJIwgEYMno6cOEgIe4cY+JSQr6OC9yjRNqcsgRlMGKH9B3QrmPYiPbKM9NqTqcE9KaktLFDmpjyUsLqNFvHc+2s2N+8x9pT3W1Ef894hcRKXBL7l26S+V+dqkWij0Ndg081JZpR1XHjkuuuqJvbX6qS5JAQp3CP4ilhrpWTPttak+naVW+Zjr/pTMWqPTe5Od7VLWnA7s9xToPmXtV1qu7pfqXmmFEXsYVt7NI8D1DDMepokPcVHvGEZ+vEurZurbvPVKtgNJv4tqyHD61DkYQ=</latexit><latexit sha1_base64="4JXcQvmS1cZ5PKlmHsv0tSJYNSM=">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</latexit><latexit sha1_base64="4JXcQvmS1cZ5PKlmHsv0tSJYNSM=">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</latexit>
  9. 9. • Class distribution of unlabeled data is not same as that of labeled data • Estimated number of unlabeled data is used for both DARP and ReMixMatch* • SSL: semi-supervised learning (not consider imbalance), RB: re-balancing (not use unlabeled data) Experiments: “Different” Class Distributions 7*bACC / GM: arithmetic / geometric mean over class-wise accuracy As the gap between distributions is increased, the improvement from DAPR is also increased *Larger ⟹ More severely imbalanced<latexit sha1_base64="4JXcQvmS1cZ5PKlmHsv0tSJYNSM=">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</latexit><latexit sha1_base64="4JXcQvmS1cZ5PKlmHsv0tSJYNSM=">AAACyXicjVHLSsNAFD2Nr1pfVZdugkVwVRIRdFlwI7ipYB/Qikym0xqbl8lErMWVP+BWf0z8A/0L74xTUIvohCRnzr3nzNx7vSTwM+k4rwVrZnZufqG4WFpaXlldK69vNLM4T7lo8DiI07bHMhH4kWhIXwainaSChV4gWt7wSMVbNyLN/Dg6k6NEnIdsEPl9nzNJVLM7YGHILsoVp+roZU8D14AKzKrH5Rd00UMMjhwhBCJIwgEYMno6cOEgIe4cY+JSQr6OC9yjRNqcsgRlMGKH9B3QrmPYiPbKM9NqTqcE9KaktLFDmpjyUsLqNFvHc+2s2N+8x9pT3W1Ef894hcRKXBL7l26S+V+dqkWij0Ndg081JZpR1XHjkuuuqJvbX6qS5JAQp3CP4ilhrpWTPttak+naVW+Zjr/pTMWqPTe5Od7VLWnA7s9xToPmXtV1qu7pfqXmmFEXsYVt7NI8D1DDMepokPcVHvGEZ+vEurZurbvPVKtgNJv4tqyHD61DkYQ=</latexit><latexit sha1_base64="4JXcQvmS1cZ5PKlmHsv0tSJYNSM=">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</latexit><latexit sha1_base64="4JXcQvmS1cZ5PKlmHsv0tSJYNSM=">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</latexit>
  10. 10. • We investigate imbalanced SSL, which is an important but under-explored • We identify that current SSL algorithms can be suffered under such a scenario • We propose a simple, yet effective pseudo-label refining method (DARP) Summary In our paper, there are • Formal derivation and proof • Detailed analysis • More experiments • Results on other dataset Thank you for your attention !

Official slides for the NeurIPS 2020 paper "Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning," by Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang, Sung Ju Hwang, and Jinwoo Shin.

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