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pro vyhledávání: '"Richardson, Eitan"'
Autor:
Shalev-Arkushin, Rotem, Azulay, Aharon, Halperin, Tavi, Richardson, Eitan, Bermano, Amit H., Fried, Ohad
Diffusion-based generative models have recently shown remarkable image and video editing capabilities. However, local video editing, particularly removal of small attributes like glasses, remains a challenge. Existing methods either alter the videos
Externí odkaz:
http://arxiv.org/abs/2406.14510
Autor:
Richardson, Eitan, Weiss, Yair
Unsupervised image-to-image translation is an inherently ill-posed problem. Recent methods based on deep encoder-decoder architectures have shown impressive results, but we show that they only succeed due to a strong locality bias, and they fail to l
Externí odkaz:
http://arxiv.org/abs/2007.12568
Autor:
Richardson, Eitan, Weiss, Yair
Since the discovery of adversarial examples - the ability to fool modern CNN classifiers with tiny perturbations of the input, there has been much discussion whether they are a "bug" that is specific to current neural architectures and training metho
Externí odkaz:
http://arxiv.org/abs/2002.08859
Autor:
Richardson, Eitan, Weiss, Yair
A longstanding problem in machine learning is to find unsupervised methods that can learn the statistical structure of high dimensional signals. In recent years, GANs have gained much attention as a possible solution to the problem, and in particular
Externí odkaz:
http://arxiv.org/abs/1805.12462
Akademický článek
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Autor:
Richardson, Eitan, Werman, Michael
Publikováno v:
In Pattern Recognition Letters 1 November 2014 49:99-106
Publikováno v:
2014 11th IEEE International Conference on Advanced Video & Signal Based Surveillance (AVSS); 2014, p13-18, 6p