Synthetic Data Generation and Differential Privacy using Tensor Networks' Matrix Product States (MPS)

Recent advancements in Latent Diffusion Models (LDMs) have prompted the introduction of the Posterior Collapse Attack (PCA), a novel framework aimed at protecting images from unauthorized manipulation. This approach draws on the posterior collapse phenomenon observed in Variational Autoencoder (VAE) training, highlighting two distinct collapse types: diffusion collapse and concentration collapse.

Recent advancements in reward feedback learning (ReFL) highlight the effectiveness of video generation models as latent reward models, addressing significant challenges in aligning video generation with human preferences. Traditional video reward models have limitations due to their reliance on pixel-space inputs, which complicate the optimization process and increase memory usage.

A new method called DEMIST has been introduced for synthesizing quantitative magnetization transfer (qMT) maps, specifically pool size ratio (PSR) maps, from standard T1-weighted and FLAIR images using a 3D latent diffusion model. This approach utilizes a two-stage process involving separate autoencoders and a conditional diffusion model with decoupled conditioning mechanisms.

OmniRefiner has been introduced as a detail-aware refinement framework aimed at improving reference-guided image generation. This framework addresses the limitations of current diffusion models, which often fail to retain fine-grained visual details during image refinement due to inherent VAE-based latent compression issues. By employing a two-stage correction process, OmniRefiner enhances pixel-level consistency and structural fidelity in generated images.

A new model called SPINRec has been introduced to enhance explanation fidelity in recommender systems, addressing the gap in accurately reflecting a model's reasoning. This model employs stochastic baseline sampling to generate personalized and stable explanations by integrating multiple user profiles from empirical data.

The STCDiT framework has been introduced as a novel video super-resolution solution that utilizes a pre-trained video diffusion model to enhance video quality by restoring structural and temporal integrity from degraded inputs, particularly under complex camera movements. This method employs a motion-aware VAE reconstruction technique to achieve segment-wise reconstruction, ensuring uniform motion characteristics within each segment.