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Found 8 papers, 0 papers with code
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps
First, we theoretically show the transferability of robustness from an adversarially trained teacher model to a student model with the help of mixup augmentation.
NASOA: Towards Faster Task-oriented Online Fine-tuning with a Zoo of Models
Fine-tuning from pre-trained ImageNet models has been a simple, effective, and popular approach for various computer vision tasks.
Towards a Theoretical Framework of Out-of-Distribution Generalization
We also introduce a new concept of expansion function, which characterizes to what extent the variance is amplified in the test domains over the training domains, and therefore give a quantitative meaning of invariant features.
Out-of-Distribution Generalization Analysis via Influence Function
One of the definitions of OOD accuracy is worst-domain accuracy.
MetaAugment: Sample-Aware Data Augmentation Policy Learning
Automated data augmentation has shown superior performance in image recognition.
Provable More Data Hurt in High Dimensional Least Squares Estimator
Furthermore, the finite-sample distribution and the confidence interval of the prediction risk are provided.
Risk Variance Penalization
The key of the out-of-distribution (OOD) generalization is to generalize invariance from training domains to target domains.
