3 citations · 6 across the 20 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Connecting adversarial attacks and optimal transport for domain adaptation
Arip Asadulaev, Vitaly Shutov, Alexander Korotin +2
We present a novel algorithm for domain adaptation using optimal transport. In domain adaptation, the goal is to adapt a classifier trained on the source domain samples to the targ…
Neural Optimal Transport with General Cost Functionals
Arip Asadulaev, Alexander Korotin, Vage Egiazarian +2
We introduce a novel neural network-based algorithm to compute optimal transport (OT) plans for general cost functionals. In contrast to common Euclidean costs, i.e., or $…