.png){kind=icon}
Return
Adversarial data splitting for domain generalization
.png){kind=icon}
PRE
AI
DOI：10.1007/s11432-022-3857-5.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Domain generalization aims to learn a model that is generalizable to an unseen target domain, which is a fundamental and challenging task in machine learning for out-of-distribution generalization. This paper proposes a novel domain generalization approach that enforces the learned model to be able to generalize well over the train/val subset splitting of the training dataset. This idea is modeled herein as an adversarial data splitting framework, formulated as a min-max optimization problem inspired by the meta-learning approach. The min-max optimization problem is solved by iteratively splitting the training dataset into the training and val subsets to maximize the domain shift measured by the objective function and updating the model parameters to enable the model to generalize well from the training subset to the val subset by minimizing the objective function. This adversarial training approach does not assume the known domain labels of the training data; instead, it automatically investigates the hard splitting of the train/val subsets to learn the generalizable model. Extensive experimental results using three benchmark datasets demonstrate the superiority of this approach. In addition, we derive a generalization error bound for the theoretical understanding of our proposed approach.
Keywords:
domain generalization
adversarial learning
data splitting
meta-learning
out of distribution
Journal
IF:
7.6
Papers:
4.9K
Citations:
8.9K
Organization
No organization information available
Cited Papers
ShareSaveShareSaveVisuomotor perimetry in fish: A new approach to the functional analysis of altered visual pathways
PREAI
ShareSave
ShareSaveProgressive learning with multi-scale attention network for cross-domain vehicle re-identification
PREAI
ShareSaveA cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction.png)
OAAI
IF0
OAAIShareSaveShareSave
View more