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Complementary Experts for Effective Long-Tailed Semi-Supervised Learning


Core Concepts
To address the challenge of long-tailed semi-supervised learning, where labeled data exhibit imbalanced class distribution and unlabeled data follow an unknown distribution, the authors propose a novel method named ComPlementary Experts (CPE) that trains multiple experts to model various class distributions, each yielding high-quality pseudo-labels within one form of class distribution. They also introduce Classwise Batch Normalization to avoid performance degradation caused by feature distribution mismatch between head and non-head classes.
Abstract
The authors address the problem of Long-Tailed Semi-Supervised Learning (LTSSL), where labeled data exhibit imbalanced class distribution and unlabeled data follow an unknown distribution. Unlike in balanced SSL, the generated pseudo-labels are skewed towards head classes, intensifying the training bias. This phenomenon is even amplified as more unlabeled data will be mislabeled as head classes when the class distribution of labeled and unlabeled datasets are mismatched. To solve this problem, the authors propose a novel method named ComPlementary Experts (CPE). Specifically, they train multiple experts to model various class distributions, each of them yielding high-quality pseudo-labels within one form of class distribution. Besides, they introduce Classwise Batch Normalization for CPE to avoid performance degradation caused by feature distribution mismatch between head and non-head classes. The authors evaluate CPE on CIFAR-10-LT, CIFAR-100-LT, and STL-10-LT dataset benchmarks. They show that CPE achieves state-of-the-art performances, improving test accuracy by over 2.22% compared to baselines on CIFAR-10-LT.
Stats
The authors report the following key metrics and figures: On CIFAR-10-LT with (N1, M1) = (1500, 3000) and γ = 150, CPE surpasses the previous SOTA method (ACR) by 0.44 percentage points (pp), and all other baselines by 1.31 pp. On CIFAR-100-LT, the performances of CPE is on par with ACR, but beats other baselines by >1.51 pp. On CIFAR-10-LT with (N1, M1) = (500, 400) and (γl, γu) = (100, 1), CPE surpasses ACR by 1.22 pp and other baselines by up to >8.01 pp.
Quotes
"To solve this problem, we propose a novel method named ComPlementary Experts (CPE)." "Besides, we introduce Classwise Batch Normalization for CPE to avoid performance degradation caused by feature distribution mismatch between head and non-head classes."

Key Insights Distilled From

by Chengcheng M... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2312.15702.pdf
Three Heads Are Better Than One

Deeper Inquiries

How can the proposed CPE method be extended to handle more complex class distributions in the unlabeled data, such as multi-modal distributions

The proposed ComPlementary Experts (CPE) method can be extended to handle more complex class distributions in the unlabeled data, such as multi-modal distributions, by introducing additional experts specialized in modeling different modes of the distribution. Instead of just three experts, the model can be expanded to include more experts, each trained to capture a specific mode or cluster within the unlabeled data distribution. By diversifying the experts to cover various modes, the CPE method can adapt to the complexity of multi-modal distributions and generate high-quality pseudo-labels for each mode. This extension would involve adjusting the logit adjustments and training each expert with the appropriate intensity to handle the specific characteristics of each mode within the distribution.

What are the potential limitations of the Classwise Batch Normalization mechanism, and how can it be further improved to better handle feature distribution mismatches

The Classwise Batch Normalization (CBN) mechanism, while effective in handling feature distribution mismatches between head and tail classes, may have limitations in scenarios where the feature distributions are highly complex or non-linear. One potential limitation is the assumption of linear separability within the feature space, which may not hold true for all datasets with intricate feature distributions. To improve CBN and address these limitations, one approach could be to incorporate non-linear transformations or adaptive normalization techniques within the CBN layers. This enhancement would allow CBN to adapt to non-linear feature distributions and capture more intricate relationships between features in different classes. Additionally, exploring more advanced normalization methods, such as group normalization or instance normalization, could further enhance the capability of CBN to handle diverse feature distributions.

How can the insights from this work on long-tailed semi-supervised learning be applied to other machine learning tasks with imbalanced data, such as few-shot learning or domain adaptation

The insights from this work on long-tailed semi-supervised learning can be applied to other machine learning tasks with imbalanced data, such as few-shot learning or domain adaptation, by leveraging similar strategies to address class imbalances and distribution mismatches. In few-shot learning, where the task is to learn from a limited number of examples per class, techniques like logit adjustment and multi-expert modeling can help improve the generalization performance on underrepresented classes. By adapting the CPE framework to few-shot learning, models can better handle the imbalance in class frequencies and generate more accurate predictions for rare classes. Similarly, in domain adaptation, where the source and target domains may have different class distributions, methods like CBN can be utilized to align feature distributions across domains and improve the transferability of the learned representations. By incorporating the principles of long-tailed semi-supervised learning, these tasks can benefit from enhanced performance on imbalanced datasets and distribution shifts.
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