Reweighting balanced representation learning for long tailed image recognition in multiple domains.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Multi-domain long-tailed learning presents challenges due to within-domain class imbalance and across-domain sample proportion variation.
• These imbalances introduce significant biases in covariate and representation learning, hindering the extraction of domain-invariant features.

Purpose of the Study:

• To apply and evaluate an advanced reweighting balanced representation learning (BRL) algorithm for multi-domain long-tailed image recognition.
• To address biases in both input and latent spaces caused by data imbalance.

Main Methods:

• The study integrates covariate and representation balancing techniques within a reweighting-based class balancing framework.
• The balanced representation learning (BRL) algorithm is applied to multi-domain long-tailed image recognition tasks.

Main Results:

• Extensive evaluations on six benchmark datasets demonstrate BRL's effectiveness.
• The algorithm successfully extracts domain- and class-unbiased feature representations.
• Significant improvements in classifier performance were observed, especially for the most imbalanced classes.

Conclusions:

• The balanced representation learning (BRL) algorithm offers a robust solution for multi-domain long-tailed image recognition.
• This approach shows promise for applications in fields like environmental monitoring and medical imaging, addressing critical data imbalance issues.