LEAwareSGD modulates the learning rate with a Lyapunov exponent estimate and claims state-of-the-art accuracy on three single-domain generalization benchmarks, but the method is underspecified and its hyperparameters are inconsistent.
Adversarial bayesian augmentation for single-source domain generaliza- tion
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
baseline 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
REJECT 1roles
baseline 1polarities
contest 1representative citing papers
citing papers explorer
-
Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization
LEAwareSGD modulates the learning rate with a Lyapunov exponent estimate and claims state-of-the-art accuracy on three single-domain generalization benchmarks, but the method is underspecified and its hyperparameters are inconsistent.