Anchor Data Augmentation (ADA) enhances data robustness by utilizing anchor variables to structure data intelligently, improving model performance under diverse conditions.
Utilizing expert knowledge to select relevant features for anchor variables maximizes the effectiveness of Anchor Data Augmentation, leading to better handling of data discrepancies.
By partitioning datasets based on feature similarity through clustering or binning, ADA effectively accommodates heterogeneity, thereby preserving the inherent nonlinear structures in data.
The experiments demonstrate that Anchor Data Augmentation outperforms traditional methods like C-Mixup by providing superior in-distribution generalization and out-of-distribution robustness.
Collection
[
|
...
]