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Found 3 papers, 1 papers with code
Contrastive Pretraining for Visual Concept Explanations of Socioeconomic Outcomes
This improves the model's interpretability as it enables the latent space of the model to associate urban concepts with continuous intervals of socioeconomic outcomes.
Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing
In recent years, black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in Remote Sensing.
Exploring Self-Attention for Crop-type Classification Explainability
Using explainable machine learning to reveal the inner workings of these models is an important step towards improving stakeholders' trust and efficient agriculture monitoring.
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