GAPL learns a compact set of canonical forgery prototypes and applies two-stage LoRA training to build a low-variance feature space that improves generalization across GAN and diffusion generators.
Swin transformer: Hierarchical vision transformer using shifted windows
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DropsToGrid is a spatio-temporal neural process that integrates temporal sequences from noisy irregular stations with spatial radar context to produce dense stochastic rainfall fields with calibrated uncertainty, outperforming baselines even with few stations or across regions.
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Scaling Up AI-Generated Image Detection with Generator-Aware Prototypes
GAPL learns a compact set of canonical forgery prototypes and applies two-stage LoRA training to build a low-variance feature space that improves generalization across GAN and diffusion generators.
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From Drops to Grid: Noise-Aware Spatio-Temporal Neural Process for Rainfall Estimation
DropsToGrid is a spatio-temporal neural process that integrates temporal sequences from noisy irregular stations with spatial radar context to produce dense stochastic rainfall fields with calibrated uncertainty, outperforming baselines even with few stations or across regions.