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1. Color palette generation with Conditional Generative Adversarial Networks
SERGEI BURYKIN, 2021, diplomsko delo

Ključne besede: Artificial Intelligence, Deep Learning, Generative Adversarial Networks
Celotno besedilo (datoteka, 935,97 KB)

2. Lesion filling in magnetic resonance images based on generative adversarial networks
MOJCA BERTONCELJ, 2019, magistrsko delo/naloga

Ključne besede: lesion filling, magnetic resonance, image analysis, deep neural networks, generative adversarial networks
Celotno besedilo (datoteka, 10,25 MB)

3. k-Same-Net
Blaž Meden, Žiga Emeršič, Vitomir Štruc, Peter Peer, 2018, izvirni znanstveni članek

Ključne besede: face deidentification, generative neural networks, k-Same algorithm
Celotno besedilo (datoteka, 2,92 MB) Gradivo ima več datotek! Več...

4. Improved Mammography Classification using Data Augmentation and Transfer Learning
ANA PETERKA, 2022, diplomsko delo

Ključne besede: Artificial Intelligence, Machine Learning, Deep Learning, Convolutional Neural Networks, Generative Adversarial Networks, Mammography, Cancer Detection.
Celotno besedilo (datoteka, 1,02 MB)

5. Generative adversarial model based augmentation of angiographic images for improved detection of intracranial aneurysms
DAVID KRIŽAJ, 2023, magistrsko delo/naloga

Ključne besede: aneurysm detection, computer vision, convolutional neural networks, generative adversarial networks, dataset augmentation
Celotno besedilo (datoteka, 3,91 MB)

6. Conditional generative positive and unlabeled learning
Aleš Papič, Igor Kononenko, Zoran Bosnić, 2023, izvirni znanstveni članek

Ključne besede: positive and unlabeled learning, partially supervised learning, generative adversarial networks, deep learning
Celotno besedilo (datoteka, 2,35 MB) Gradivo ima več datotek! Več...

7. Face deidentification with generative neural networks
Blaž Meden, 2023, doktorska disertacija

Ključne besede: face deidentification, generative neural networks, facial synthesis, privacy protection, data utility, image-based biometrics
Celotno besedilo (datoteka, 7,15 MB)
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