Label Noise Robust Image Representation Learning based on Supervised
  Variational Autoencoders in Remote Sensing
Gencer Sumbul Begüm Demir
Due to the publicly available thematic maps and crowd-sourced data, remote
sensing (RS) image annotations can be gathered at zero cost for training deep
neural networks (DNNs). However, such annotation sources may increase the risk
of including noisy labels in training data, leading to inaccurate RS image
representation learning (IRL). To address this issue, in this paper we propose
a label noise robust IRL method that aims to prevent the interference of noisy
labels on IRL, independently from the learning task being considered in RS. To
this end, the proposed method combines a supervised variational autoencoder
(SVAE) with any kind of DNN. This is achieved by defining variational
generative process based on image features. This allows us to define the
importance of each training sample for IRL based on the loss values acquired
from the SVAE and the task head of the considered DNN. Then, the proposed
method imposes lower importance to images with noisy labels, while giving
higher importance to those with correct labels during IRL. Experimental results
show the effectiveness of the proposed method when compared to well-known label
noise robust IRL methods applied to RS images. The code of the proposed method
is publicly available at https://git.tu-berlin.de/rsim/RS-IRL-SVAE.