The Impact of Residual Layers and Inception Modules on Meta-Learning

Abstract

This paper presents an algorithm with which artificial agents should be able to learn and adapt quickly from only a few examples. This algorithm respects the initial goal of neural networks to be able to learn and adapt in time. However, the work is just in the beginning and the best results have not been reached yet. This paper aims to present the impact of residual layers and inception modules on meta-learning in order to obtain an improvement of results.