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Adversarial training of neural networks

01UJBRV

A.A. 2019/20

Course Language

Inglese

Course degree

Doctorate Research in Ingegneria Elettrica, Elettronica E Delle Comunicazioni - Torino

Course structure
Teaching Hours
Lezioni 15
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Valsesia Diego   Ricercatore L240/10 ING-INF/03 15 0 0 0 3
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
*** N/A ***    
2019/20
PERIOD: MAY - JUNE Generative Adversarial Networks (GANs) have achieved impressive results in generating fake images that look realistic. GANs introduced the concept of adversarial training where two neural networks are trained by playing a game against each other. Adversarial training is a powerful concept that has been applied to wide variety of problems across different fields including generative models for images, time series, or DNA/protein sequences, unsupervised image-to-image translation, domain adaptation, regularization of inverse problems and many more. This course will provide students with the theoretical foundations of adversarial training as well as the most recent practical examples of its use. The target audience is cross-disciplinary as adversarial training has proved itself to be a staple of modern deep learning across many fields.
PERIOD: MAY - JUNE Generative Adversarial Networks (GANs) have achieved impressive results in generating fake images that look realistic. GANs introduced the concept of adversarial training where two neural networks are trained by playing a game against each other. Adversarial training is a powerful concept that has been applied to wide variety of problems across different fields including generative models for images, time series, or DNA/protein sequences, unsupervised image-to-image translation, domain adaptation, regularization of inverse problems and many more. This course will provide students with the theoretical foundations of adversarial training as well as the most recent practical examples of its use. The target audience is cross-disciplinary as adversarial training has proved itself to be a staple of modern deep learning across many fields.
• Brief review of neural network architectures and training via the backpropagation algorithm • Generative Adversarial Networks (GANs): introduction and game-theoretical formulation • Wasserstein GANs: stability improvements via optimal transport formulation • Generative models: recent advances in image generation (Progressive Growing, BigGAN), point cloud generation (GraphCNN), optimized DNA sequences • Image-to-image and video-to-video translation: style transfer and Cycle GANs • Inverse problems: image inpainting, superresolution, compressed sensing
• Brief review of neural network architectures and training via the backpropagation algorithm • Generative Adversarial Networks (GANs): introduction and game-theoretical formulation • Wasserstein GANs: stability improvements via optimal transport formulation • Generative models: recent advances in image generation (Progressive Growing, BigGAN), point cloud generation (GraphCNN), optimized DNA sequences • Image-to-image and video-to-video translation: style transfer and Cycle GANs • Inverse problems: image inpainting, superresolution, compressed sensing
Wednesday 11/3 9:00-12:00 Aula C Wednesday 18/3 14:00-17:00 Aula C Wednesday 25/3 14:00-17:00 Aula C Wednesday 1/4 14:00-17:00 Aula C Wednesday 8/4 14:00-17:00 Aula C ATTENZIONE: LE LEZIONI VERRANNO RIPROGRAMMATE PER IL PERIODO MAGGIO - GIUGNO
Wednesday 11/3 9:00-12:00 Aula C Wednesday 18/3 14:00-17:00 Aula C Wednesday 25/3 14:00-17:00 Aula C Wednesday 1/4 14:00-17:00 Aula C Wednesday 8/4 14:00-17:00 Aula C ATTENZIONE: LE LEZIONI VERRANNO RIPROGRAMMATE PER IL PERIODO MAGGIO - GIUGNO
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