Self-Supervised Learning for Covariance Estimation
Tzvi Diskin Ami Wiesel
We consider the use of deep learning for covariance estimation. We propose to
globally learn a neural network that will then be applied locally at inference
time. Leveraging recent advancements in self-supervised foundational models, we
train the network without any labeling by simply masking different samples and
learning to predict their covariance given their surrounding neighbors. The
architecture is based on the popular attention mechanism. Its main advantage
over classical methods is the automatic exploitation of global characteristics
without any distributional assumptions or regularization. It can be pre-trained
as a foundation model and then be repurposed for various downstream tasks,
e.g., adaptive target detection in radar or hyperspectral imagery.