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Found 3 papers, 3 papers with code
Cyber Attack Detection thanks to Machine Learning Algorithms
The Random Forest Classifier succeeds in detecting more than 95% of the botnets in 8 out of 13 scenarios and more than 55% in the most difficult datasets.
Synthetic Magnetic Resonance Images with Generative Adversarial Networks
Data augmentation is essential for medical research to increase the size of training datasets and achieve better results.
Segmentation and Generation of Magnetic Resonance Images by Deep Neural Networks
Three main architectures are described: Deep Convolution GAN (DCGAN), Super Resolution Residual GAN (SRResGAN) and Progressive GAN (ProGAN), and five loss functions are tested: the Original loss, LSGAN, WGAN, WGAN_GP and DRAGAN.
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