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 intra-class/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. We further show that our method can be flexibly adapted to long-tail recognition tasks, demonstrating its versatility in building more generalizable models. Code is available at https://github.com/nerdslab/LatentDR.

通过在潜变空间中降级样本并从其受损版本中恢复，我们的方法通过增强标签的方式引导样本扩充过程，以解决深度学习模型在新的、未知领域中推广的挑战，从而促进了多样的类内/跨域变异性，并在潜变空间扩充中呈现比现有方法更显著的改善，展示了在构建更具普适性的模型中更大的灵活性。

LatentDR: 通过样本感知的隐式退化和恢复改善模型的泛化能力