Impact of Spectral Adversarial Perturbations on Conditional Diffusion Model and GAN Alignment in Tabular Data Generation

Generating high-fidelity and biologically plausible synthetic single-cell RNA sequencing (scRNA-seq) data, especially with conditional control, is challenging due to its high dimensionality, sparsity, and complex biological variations. Existing generative models often struggle to capture these unique characteristics and ensure robustness to structural noise in cellular networks. We introduce LapDDPM, a novel conditional Graph Diffusion Probabilistic Model for robust and high-fidelity scRNA-seq generation. LapDDPM uniquely integrates graph-based representations with a score-based diffusion mode

Research goal: What is the impact of spectral adversarial perturbations on the alignment capabilities of conditional diffusion models versus GANs in tabular data generation tasks, as measured by robustness metrics (e.g., AUROC, adversarial accuracy)?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.5/10.