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.