Learning image segmentation from few annotations

How to build machine learning models from few annotations is an open research question. This article shows an application of a metalearning algorithm (REPTILE) to solve the problem of object segmentation. We evaluate how using REPTILE during a pre-training phase accelerates the learning process without loosing performance of the resulting segmentation in poor labeling conditions, and compare these results against training the detectors using basic transfer learning. Two scenarios are tested: (i) how segmentation performance evolves through training epochs with a fixed amount of labels and (ii) how segmentation performance improves with an increasing amount of labels after a fixed amount of epochs. The results suggest that REPTILE is useful making learning faster in both cases.