JIVE: Jacobian-Informed Volume Expansion for Diverse Generative Sampling

arXiv:2609.33906 · cs.LG, cs.AI, cs.CV · Submitted 2026-09-27 · Read on arXiv

cs.LG, cs.AI, cs.CV

Submitted: 2026-09-27

Updated: 2026-09-27

Code: https://github.com/guangxunZhang/JIVE

Terminology

• Flow Matching — 13× in this paper · explained in 8 episodes
• Alignment — 12× in this paper · explained in 1 episode
• Diffusion Model — 8× in this paper · explained in 8 episodes
• Diffusion Models — 8× in this paper · explained in 6 episodes
• Fidelity — 8× in this paper · explained in 2 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• Generative Models — 5× in this paper · explained in 2 episodes
• Trace — 5× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Rectified Flow — 3× in this paper · explained in 3 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Flow Matching Model — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Jacobian Vector Product — 1× in this paper
• Latent Space — 1× in this paper · explained in 7 episodes
• Mode Collapse — 1× in this paper · explained in 2 episodes
• Prompt Adherence — 1× in this paper · explained in 2 episodes
• Training-Free Framework — 1× in this paper · explained in 2 episodes

Sources

• Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis
• STRIDE: Training-Free Diversity Guidance via PCA-Directed Feature Perturbation in Single-Step Diffusion Models
• LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
• Teacher-Feature Drifting: One-Step Diffusion Distillation with Pretrained Diffusion Representations

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