Image generation using deep adversarial generative models on graphs
keywords DEEP LEARNING, VIDEO ANALYSIS
Reference persons ENRICO MAGLI
Thesis type RESEARCH
Description Convolutional neural networks (CNNs) have enjoyed great success in tasks such as image classification, object detection, etc thanks to weight sharing, invariances to geometric transformations and hierarchical
decompositions induced by the convolution operations. Recently, generative adversarial networks (GANs) have used CNNs to learn to generate new images by learning a mapping between a latent space where
random vectors are generated and a "real" image. We have recently developed a GAN network for data defined on graphs. This model is well suited to represent dependencies among pixels of an image. The objective of this thesis is to develop a graph-based GAN that learns to generate natural images.
Required skills Candidate students should have a background in ICT or mathematics. Knowledge of neural networks (including TensorFlow environment and Python programming) is not a prerequisite, although it would help in the initial stage of the activity.
Deadline 26/09/2019 PROPONI LA TUA CANDIDATURA