Art Arena evaluates how artistic styles from training data leak into AI-generated images without explicit prompts, revealing asymmetric blending due to differences in representational strength and interaction dynamics across models like Stable Diffusion.
On memorization in diffusion models
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
Higher-variance classes are learned first in diffusion models; strong class imbalance reverses the order and imposes distinct delayed learning times on minority classes.
Presents SimA, a score-based single-query membership inference attack for diffusion models and LDMs that uses denoiser output norm to reveal training set proximity and outperforms multi-query baselines on eight datasets.
BAF reduces memorization in diffusion LoRAs by filtering spectral channels of the adaptation weights that show weak alignment with the base model's principal subspace.
Transient Turn Injection is a new attack that evades LLM moderation by spreading harmful intent over multiple isolated turns using automated agents.
Diffusion models overfit denoising loss at intermediate noise but generalize in inference as model error smooths the flow field and sampling paths avoid memorized noisy training data.
A PDE framework using Li-Yau inequalities proves well-posedness and sharp stability for score-based Fokker-Planck dynamics, with reverse-time trajectories concentrating on compactly supported data manifolds at rate sqrt(t).
LeakyCLIP reconstructs images from CLIP embeddings with over 258% SSIM gain versus baselines and enables membership inference from reconstruction metrics on LAION-2B data.
Derives closed-form optimal loss for unified diffusion models, provides variance-controlled estimators, and shows improved diagnosis, training schedules, and power-law scaling after subtracting the optimal value.
citing papers explorer
-
The Silent Brush: Evaluating Artistic Style Leakage in AI Art Generation
Art Arena evaluates how artistic styles from training data leak into AI-generated images without explicit prompts, revealing asymmetric blending due to differences in representational strength and interaction dynamics across models like Stable Diffusion.
-
The Interplay of Data Structure and Imbalance in the Learning Dynamics of Diffusion Models
Higher-variance classes are learned first in diffusion models; strong class imbalance reverses the order and imposes distinct delayed learning times on minority classes.
-
Score-based Membership Inference on Diffusion Models
Presents SimA, a score-based single-query membership inference attack for diffusion models and LDMs that uses denoiser output norm to reveal training set proximity and outperforms multi-query baselines on eight datasets.
-
Filtering Memorization from Parameter-Space in Diffusion Models
BAF reduces memorization in diffusion LoRAs by filtering spectral channels of the adaptation weights that show weak alignment with the base model's principal subspace.
-
Transient Turn Injection: Exposing Stateless Multi-Turn Vulnerabilities in Large Language Models
Transient Turn Injection is a new attack that evades LLM moderation by spreading harmful intent over multiple isolated turns using automated agents.
-
Diffusion Models Memorize in Training -- and Generalize in Inference
Diffusion models overfit denoising loss at intermediate noise but generalize in inference as model error smooths the flow field and sampling paths avoid memorized noisy training data.
-
A PDE Perspective on Generative Diffusion Models
A PDE framework using Li-Yau inequalities proves well-posedness and sharp stability for score-based Fokker-Planck dynamics, with reverse-time trajectories concentrating on compactly supported data manifolds at rate sqrt(t).
-
LeakyCLIP: Extracting Training Data from CLIP
LeakyCLIP reconstructs images from CLIP embeddings with over 258% SSIM gain versus baselines and enables membership inference from reconstruction metrics on LAION-2B data.
-
Diagnosing and Improving Diffusion Models by Estimating the Optimal Loss Value
Derives closed-form optimal loss for unified diffusion models, provides variance-controlled estimators, and shows improved diagnosis, training schedules, and power-law scaling after subtracting the optimal value.