Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Filchenkov, Andrey"'
Autor:
Asadulaev, Arip, Korst, Rostislav, Korotin, Alexander, Egiazarian, Vage, Filchenkov, Andrey, Burnaev, Evgeny
We propose a novel algorithm for offline reinforcement learning using optimal transport. Typically, in offline reinforcement learning, the data is provided by various experts and some of them can be sub-optimal. To extract an efficient policy, it is
Externí odkaz:
http://arxiv.org/abs/2410.14069
Nowadays, there are many diffusion and autoregressive models that show impressive results for generating images from text and other input domains. However, these methods are not intended for ultra-high-resolution image synthesis. Vector graphics are
Externí odkaz:
http://arxiv.org/abs/2306.06441
Neural style transfer draws researchers' attention, but the interest focuses on bitmap images. Various models have been developed for bitmap image generation both online and offline with arbitrary and pre-trained styles. However, the style transfer b
Externí odkaz:
http://arxiv.org/abs/2303.03405
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 settings, we demonstrate that replacing the source domain with adversari
Externí odkaz:
http://arxiv.org/abs/2207.08948
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 classifies correctly with high confidence. In our paper, we are making the
Externí odkaz:
http://arxiv.org/abs/2207.08940
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 target domain. In our method, we use optimal transport to map target sampl
Externí odkaz:
http://arxiv.org/abs/2205.15424
Generative Adversarial Networks (GAN) have motivated a rapid growth of the domain of computer image synthesis. As almost all the existing image synthesis algorithms consider an image as a pixel matrix, the high-resolution image synthesis is complicat
Externí odkaz:
http://arxiv.org/abs/2205.07301
Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling
According to recent studies, commonly used computer vision datasets contain about 4% of label errors. For example, the COCO dataset is known for its high level of noise in data labels, which limits its use for training robust neural deep architecture
Externí odkaz:
http://arxiv.org/abs/2108.00784