Reweighting balanced representation learning for long tailed image recognition in multiple domains
Abstract In multi-domain long-tailed learning, data imbalance appears in two ways: within-domain class imbalance and across-domain sample proportion variation. These imbalances introduce biases in covariates and representations when learning domain-invariant features in both input and latent spaces....
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-03459-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract In multi-domain long-tailed learning, data imbalance appears in two ways: within-domain class imbalance and across-domain sample proportion variation. These imbalances introduce biases in covariates and representations when learning domain-invariant features in both input and latent spaces. This paper applies an advanced reweighting balanced representation learning (BRL) algorithm to multi-domain long-tailed image recognition. By integrating covariate and representation balancing techniques into a reweighting-based class balancing approach, BRL effectively addresses these biases. Extensive evaluation on six benchmark datasets confirms its ability to extract domain- and class-unbiased feature representations, leading to excellent classifier performance, especially for the hardest classes. This approach also shows potential for applications in areas such as environmental monitoring and medical imaging, providing a robust solution with broad scientific implications. |
|---|---|
| ISSN: | 2045-2322 |