1 code implementation • 17 Jan 2020 • Antoine Delplace
Data augmentation is essential for medical research to increase the size of training datasets and achieve better results.
2 code implementations • 17 Jan 2020 • Antoine Delplace, Sheryl Hermoso, Kristofer Anandita
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.
2 code implementations • 10 Jan 2020 • Antoine Delplace
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.