PyTorch implementation of LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration, WACV 2024
Ran Liu, Sahil Khose, Jingyun Xiao, Lakshmi Sathidevi, Keerthan Ramnath, Zsolt Kira, Eva L Dyer
Despite significant advances in deep learning, models often struggle to generalize well to new, unseen domains, especially when training data is limited.
To address this challenge, we propose a novel approach for distribution-aware latent augmentation that leverages the relationships across samples to guide the augmentation procedure.
Our approach first degrades the samples stochastically in the latent space, mapping them to augmented labels, and then restores the samples from their corrupted versions during training. This process confuses the classifier in the degradation step and restores the overall class distribution of the original samples, promoting diverse intraclass/cross-domain variability.
We extensively evaluate our approach on a diverse set of datasets and tasks, including domain generalization benchmarks and medical imaging datasets with strong domain shift, where we show our approach achieves significant improvements over existing methods for latent space augmentation.
- Domain generalization
- Medical image classification
- Long-tail recognition
@article{liu2023latentdr,
title={LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration},
author={Liu, Ran and Khose, Sahil and Xiao, Jingyun and Sathidevi, Lakshmi and Ramnath, Keerthan and Kira, Zsolt and Dyer, Eva L},
journal={arXiv preprint arXiv:2308.14596},
year={2023}
}