Improving Contrastive Learning on Visually Homogeneous Mars Rover Images

no code implementations • 17 Oct 2022 • Isaac Ronald Ward, Charles Moore, Kai Pak, Jingdao Chen, Edwin Goh

In this study, we propose two approaches to resolve this: 1) an unsupervised deep clustering step on the Mars datasets, which identifies clusters of images containing similar semantic content and corrects false negative errors during training, and 2) a simple approach which mixes data from different domains to increase visual diversity of the total training dataset.

Contrastive Learning Deep Clustering