Flexible Distribution Alignment: Improving Long-tailed Semi-supervised Learning with Proper Calibration

Core Concepts

Introducing FlexDA for improved model calibration and alignment in long-tailed semi-supervised learning.

Abstract

The FlexDA framework addresses challenges in long-tailed semi-supervised learning by dynamically aligning predictions with the unlabeled data distribution. It introduces logit-adjusted losses for supervised and unsupervised training, promoting fair data usage and model calibration. ADELLO, based on FlexDA, outperforms state-of-the-art approaches on CIFAR100-LT, STL10-LT, and ImageNet127 datasets. The method enhances model calibration and generalization across various scenarios of label shift and class imbalance.

Stats

"Our approach progressively smooths the target prior, starting from the unlabeled prior ˆQ1(y) = ˆQ(y), and gradually transitioning to a (near) balanced prior ˆQ0(y) = 1 K by the end of the training process."
"The temperature T is calculated as exp(KL(Pbal∥ ˆQ)), adapting distillation to class imbalance."
"ADELLO demonstrates superior performance in handling unknown distribution mismatches on CIFAR100-LT."

Quotes

"Our approach progressively smooths the target prior, crucial to accurate debiasing at inference."
"Model calibration has gained recent attention for enabling effective pseudo-labeling in SSL."
"FlexDA dynamically adapts to the characteristics of unlabeled data by aligning predictions with a target distribution."

Key Insights Distilled From

Flexible Distribution Alignment

by Emanuel Sanc... at arxiv.org 03-14-2024

https://arxiv.org/pdf/2306.04621.pdf

Deeper Inquiries

How does FlexDA address challenges related to biased pseudo-labels in long-tailed semi-supervised learning

FlexDA addresses challenges related to biased pseudo-labels in long-tailed semi-supervised learning by dynamically adapting the model to align with the characteristics of unlabeled data. It adjusts the target distribution based on an Exponential Moving Average (EMA) of pseudo-labels during optimization, gradually transitioning from the labeled prior to a balanced classifier by the end of training. This approach effectively counters bias introduced by both labeled and unlabeled data samplers, ensuring robust training in scenarios with class imbalance.

What implications does model calibration have on SSL performance in scenarios with class imbalances

Model calibration plays a crucial role in SSL performance in scenarios with class imbalances as it directly impacts the accuracy and reliability of predictions. Better-calibrated models improve generalization and calibration, leading to more accurate reflection of predictive uncertainty. In long-tailed semi-supervised learning, well-calibrated models can enhance model performance by reducing misclassification errors and improving calibration metrics like Expected Calibration Error (ECE). ADELLO's superior trade-off between misclassification error and calibration error demonstrates its effectiveness in improving SSL performance under varying degrees of label shift.

How can ADELLO's flexibility in handling unknown distribution mismatches benefit practical applications beyond benchmark datasets

ADELLO's flexibility in handling unknown distribution mismatches can benefit practical applications beyond benchmark datasets by providing adaptability to diverse real-world scenarios. In practical applications where labeled and unlabeled data distributions may differ or be unknown, ADELLO's dynamic alignment with target distributions based on EMA pseudo-labels ensures effective utilization of available data for model training. This flexibility allows ADELLO to overcome challenges posed by distribution shifts without relying on strong assumptions or auxiliary classifiers, making it suitable for real-world tasks where data characteristics may vary significantly.