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.

翻译：我们探讨了深度学习在协方差估计中的应用。提出了一种全局学习神经网络的方法，该方法在推理阶段可局部化应用。借助自监督基础模型的最新进展，我们通过简单掩码不同样本并学习根据其邻近样本预测协方差的方式，实现了无需任何标注的网络训练。该架构基于流行的注意力机制，相较于传统方法的主要优势在于无需任何分布假设或正则化即可自动利用全局特征。该模型可作为基础模型进行预训练，并适用于多种下游任务，例如雷达或高光谱影像中的自适应目标检测。