Improved Deep Clustering of Mastcam Images Using Metric Learning

Tejas Panambur, Mario Parente

Abstract: In this work, we present a novel clustering method that jointly learns a triplet model and the cluster assignments of the resulting representations of image patches from data acquired by the mast cameras on the MSL Curiosity rover. Deep clustering using metric learning (DCML) iteratively clusters the features using standard K - means clustering algorithm and uses the subsequent assignments as pseudo-labels to train a triplet network with online triplet mining method. The resulting model performs better than our baseline model [1] according to visual inspection of the cluster quality and simple clustering performance measures.

External IDs:dblp:conf/igarss/PanamburP21