Multi‐Expert Dynamic Gating and Feature Decoupling Algorithm for Long‐Tail Image Classification

ABSTRACT The long‐tail distribution is characterized by a large number of samples in a few categories (head classes) and a scarcity of samples in most categories (tail classes). This inherent class imbalance significantly degrades the performance of conventional classification models, particularly on tail classes. To tackle this challenge, we propose a Multi‐Expert Dynamic Gating and Feature Decoupling Classification Algorithm based on Uniform Enhanced Sampling. The proposed method integrates multi‐expert learning with data augmentation and enhances tail classes performance by jointly optimizing the loss function and the expert assignment network. Specifically, a uniform enhanced sampling strategy is introduced to augment tail classes samples and increase their sampling frequency through resampling. During the feature learning stage, the shared layers of a convolutional network extract general features, while multiple expert models are trained independently. A feature decoupling technique is employed to separate generic and class‐specific features. In addition, a binary gating mechanism is designed to dynamically assign experts while preventing over‐reliance on specific categories. Extensive experiments on three benchmark long‐tailed classification datasets:CIFAR10‐LT, CIFAR100‐LT, and ImageNet‐LT—demonstrate that our method consistently outperforms existing state‐of‐the‐art approaches. Ablation studies further confirm the effectiveness of the uniform enhanced sampling strategy and the joint optimization of multi‐expert learning, showing that our algorithm successfully balances the model's attention across head and tail classes, thereby improving overall classification performance.