Interpretable Deep Learning refers to the design and training of deep neural networks in a way that allows humans to understand, interpret, and explain the decisions made by the model. This is especially important in critical applications where understanding the model’s reasoning is crucial.


Degradation-Resistant Unfolding Network for Heterogeneous Image Fusion

Heterogeneous image fusion (HIF) aims to enhance image quality by merging complementary information of images captured by different sensors. Early model-based approaches have strong interpretability while being limited by non-adaptive feature extractors with poor generalizability.