  Packet-loss is a common problem in data transmission, using Voice over IP.
The problem is an old problem, and there has been a variety of classical
approaches that were developed to overcome this problem. However, with the rise
of deep learning and generative models like Generative Adversarial Networks and
Autoencoders, a new avenue has emerged for attempting to solve packet-loss
using deep learning, by generating replacements for lost packets. In this
mini-survey, we review all the literature we found to date, that attempt to
solve the packet-loss in speech using deep learning methods. Additionally, we
briefly review how the problem of packet-loss in a realistic setting is
modelled, and how to evaluate Packet Loss Concealment techniques. Moreover, we
review a few modern deep learning techniques in related domains that have shown
promising results. These techniques shed light on future potentially better
solutions for PLC and additional challenges that need to be considered
simultaneously with packet-loss.
