3 papers
cs.LG2022
Adversarial Training Improves Joint Energy-Based Generative Modelling
Rostislav Korst, Arip Asadulaev
We propose the novel framework for generative modelling using hybrid energy-based models. In our method we combine the interpretable input gradients of the robust classifier and La…
cs.LG2022
Multi-step domain adaptation by adversarial attack to -divergence
Arip Asadulaev, Alexander Panfilov, Andrey Filchenkov
Adversarial examples are transferable between different models. In our paper, we propose to use this property for multi-step domain adaptation. In unsupervised domain adaptation se…
cs.LG2022
Easy Batch Normalization
Arip Asadulaev, Alexander Panfilov, Andrey Filchenkov
It was shown that adversarial examples improve object recognition. But what about their opposite side, easy examples? Easy examples are samples that the machine learning model clas…