Reframing control methods for parameters optimization in adversarial image generation.

Training procedures for deep networks require the setting of several hyper-parameters that strongly affect the obtained results. The problem is even worse in adversarial learning strategies used for image generation where a proper balancing of the discriminative and generative networks is fundamental for an effective training. In this work we propose a novel hyper-parameters optimization strategy based on the use of Proportional-Integral (PI) and Proportional-Integral-Derivative (PID) controllers.

Unsupervised Domain Adaptation of Deep Networks for ToF Depth Refinement.

Depth maps acquired with ToF cameras have a limited accuracy due to the high noise level and to the multi-path interference. Deep networks can be used for refining ToF depth, but their training requires real world acquisitions with ground truth, which is complex and expensive to collect. A possible workaround is to train networks on synthetic data, but the domain shift between the real and synthetic data reduces the performances.