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pro vyhledávání: '"Neophytou, Alexandros"'
Mitigating biases in generative AI and, particularly in text-to-image models, is of high importance given their growing implications in society. The biased datasets used for training pose challenges in ensuring the responsible development of these mo
Externí odkaz:
http://arxiv.org/abs/2406.06352
Autor:
Wang, Jianfeng, Lukasiewicz, Thomas, Massiceti, Daniela, Hu, Xiaolin, Pavlovic, Vladimir, Neophytou, Alexandros
Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this work, we adjust neural processes (NPs) to the semi-supervised image class
Externí odkaz:
http://arxiv.org/abs/2207.01066
Publikováno v:
ICASSP 2021
Convolutional neural networks (CNNs) with log-mel spectrum features have shown promising results for acoustic scene classification tasks. However, the performance of these CNN based classifiers is still lacking as they do not generalise well for unkn
Externí odkaz:
http://arxiv.org/abs/2108.06401
We propose a self-supervised method for image relighting of single view images in the wild. The method is based on an auto-encoder which deconstructs an image into two separate encodings, relating to the scene illumination and content, respectively.
Externí odkaz:
http://arxiv.org/abs/2012.06444
Autor:
Neophytou, Alexandros
This thesis addresses the problem of modeling human shape in three dimensions. Specifically, this thesis is focused on modeling body shape variation across multiple individuals, pose induced shape deformations and garment deformations that are influe
Externí odkaz:
http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.667618
Autor:
Neophytou, Alexandros, Hilton, Adrian
Publikováno v:
2014 2nd International Conference on 3D Vision; 2014, p171-178, 8p
Autor:
Neophytou, Alexandros, Hilton, Adrian
Publikováno v:
2013 International Conference on 3D Vision; 2013, p334-341, 8p
Autor:
Neophytou, Alexandros, Hilton, Adrian
Publikováno v:
Articulated Motion & Deformable Objects (9783642315664); 2012, p102-113, 12p