Clustering-Based Representation Learning through Output Translation and Its Application to Remote--Sensing Images. (arXiv:2107.05948v4 [cs.LG] UPDATED)

In supervised deep learning, learning good representations for
remote--sensing images (RSI) relies on manual annotations. However, in the area
of remote sensing, it is hard to obtain huge amounts of labeled data. Recently,
self--supervised learning shows its outstanding capability to learn
representations of images, especially the methods of instance discrimination.
Comparing methods of instance discrimination, clustering--based methods not
only view the transformations of the same image as ``positive" samples but also
similar images. In this paper, we propose a new …

application arxiv clustering images remote representation representation learning sensing translation