Relieving the Incompatibility of Network Representation and Classification for Long-Tailed Data Distribution

Area of Science:

• Computer Science
• Machine Learning
• Artificial Intelligence

Background:

• Real-world data often exhibit long-tailed distributions, posing significant challenges for training deep neural networks.
• Imbalanced datasets lead to common classes dominating training, resulting in poor accuracy for rare classes.
• Existing methods often overlook classifier adaptation, creating an incompatibility between network representation and the classifier.

Purpose of the Study:

• To address the challenges of long-tailed data distributions in deep learning.
• To propose a novel method that simultaneously optimizes network representation and classifier adaptation.
• To improve the accuracy of deep neural networks on rare classes within imbalanced datasets.

Main Methods:

• Employing knowledge distillation to tackle the long-tailed data distribution problem.
• Introducing multiexperts knowledge distillation with class-balanced sampling for joint learning.
• Utilizing a channel activation-based knowledge distillation method to further enhance performance.

Main Results:

• Achieved state-of-the-art performance on several large-scale long-tailed classification datasets.
• Demonstrated superior generalization capabilities of the proposed method.
• Successfully optimized network representation and classifier simultaneously.

Conclusions:

• The proposed knowledge distillation approach effectively solves the long-tailed data distribution problem.
• Joint optimization of representation and classifier leads to significant performance gains.
• The method offers a robust solution for deep learning with imbalanced datasets.