Impact of Gradient Penalty Constraints versus Weight Clipping on Sample Quality in Quaternion-Valued GANs Across Data Scales

Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs. However, to the best of our knowledge, a comprehensive review of recent achievements regarding deep learning for scene classification of remote sensing images is still lacking. Considering t

Research goal: What is the impact of replacing weight clipping with gradient penalty constraints in quaternion-valued GANs on sample quality metrics across varying data scales?

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