핵심 개념
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.
초록
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.
통계
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.
인용구
"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."