Národní úložiště šedé literatury Nalezeno 3 záznamů.  Hledání trvalo 0.00 vteřin. 
Reconstruction of Missing Parts of the Face Using Neural Network
Marek, Jan ; Drahanský, Martin (oponent) ; Goldmann, Tomáš (vedoucí práce)
The goal of this thesis is to design a neural network for reconstruction of face images in which a part of the face is obscured by a mask. Concepts used in the development of convolutional neural networks and generative adversarial networks are presented. Specific concepts  used in neural networks used for face reconstruction are described. The generative adversarial network presented in this thesis combines the use of gated convolutional layers and dense multiscale fusion blocks to produce realistic reconstructions of masked face images.
Reconstruction of a Damaged Facial Image
Pleško, Filip ; Orság, Filip (oponent) ; Goldmann, Tomáš (vedoucí práce)
Generative adversarial networks (GANs) are fast evolving technology in image generation field. In this thesis are GANs used for face image reconstruction, where the face was covered with some item. First some necessary theory is explained, and then existing solutions are discussed. In the end, several GAN models are proposed with intention to find out what layers combination work the best for face image reconstruction. The best solutions are combined into final architecture. The final model is also tested on face recognition task to determine whether face reconstruction can be helpful in this task.
Reconstruction of Missing Parts of the Face Using Neural Network
Marek, Jan ; Drahanský, Martin (oponent) ; Goldmann, Tomáš (vedoucí práce)
The goal of this thesis is to design a neural network for reconstruction of face images in which a part of the face is obscured by a mask. Concepts used in the development of convolutional neural networks and generative adversarial networks are presented. Specific concepts  used in neural networks used for face reconstruction are described. The generative adversarial network presented in this thesis combines the use of gated convolutional layers and dense multiscale fusion blocks to produce realistic reconstructions of masked face images.

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