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pro vyhledávání: '"Sari, Alp Eren"'
We present two practical improvement techniques for unsupervised segmentation learning. These techniques address limitations in the resolution and accuracy of predicted segmentation maps of recent state-of-the-art methods. Firstly, we leverage image
Externí odkaz:
http://arxiv.org/abs/2404.03392
We introduce Kernel Density Discrimination GAN (KDD GAN), a novel method for generative adversarial learning. KDD GAN formulates the training as a likelihood ratio optimization problem where the data distributions are written explicitly via (local) K
Externí odkaz:
http://arxiv.org/abs/2107.06197
Autor:
Almalioglu, Yasin, Turan, Mehmet, Sari, Alp Eren, Saputra, Muhamad Risqi U., de Gusmão, Pedro P. B., Markham, Andrew, Trigoni, Niki
In the last decade, numerous supervised deep learning approaches requiring large amounts of labeled data have been proposed for visual-inertial odometry (VIO) and depth map estimation. To overcome the data limitation, self-supervised learning has eme
Externí odkaz:
http://arxiv.org/abs/1911.09968
In the last decade, researchers and medical device companies have made major advances towards transforming passive capsule endoscopes into active medical robots. One of the major challenges is to endow capsule robots with accurate perception of the e
Externí odkaz:
http://arxiv.org/abs/1709.06041
Autor:
Dino, Ipek Gursel, Sari, Alp Eren, Iseri, Orcun Koral, Akin, Sahin, Kalfaoglu, Esat, Erdogan, Bilge, Kalkan, Sinan, Alatan, A. Aydin
Publikováno v:
In Automation in Construction August 2020 116
Akademický článek
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