Please enter a description about the method here

**DropAttack** is an adversarial training method that adds intentionally worst-case adversarial perturbations to both the input and hidden layers in different dimensions and minimizes the adversarial risks generated by each layer.

DropAttack

DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks

RTMDet

Parametrized learnable activation function, based on the Padé approximant.

Year	2000
Data Source	Papers with Code archive (CC BY-SA)