Controlling Dependence in Implicit Generative Models via Spread Mutual Information

arXiv:2610.10021 · stat.ML, cs.LG · Submitted 2026-10-07 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-10-07

Updated: 2026-10-07

Code: https://github.com/Linear95/CLUB

Terminology

• Accuracy — 14× in this paper · explained in 1 episode
• Adam — 12× in this paper · explained in 2 episodes
• Adaptation — 11× in this paper · explained in 1 episode
• Spread Mutual Information — 10× in this paper
• Generative Model — 8× in this paper · explained in 2 episodes
• Generative Models — 7× in this paper · explained in 2 episodes
• Representation Learning — 5× in this paper · explained in 2 episodes
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Generative AI — 4× in this paper · explained in 2 episodes
• AUC — 3× in this paper · explained in 2 episodes
• Adversarial Training — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Calibration — 3× in this paper · explained in 7 episodes
• Distribution Matching — 3× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Rectified Flow — 3× in this paper · explained in 3 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• DiffRatio: Training One-Step Diffusion Models Without Teacher Supervision
• Learning in Implicit Generative Models
• The information bottleneck method
• Representation Learning with Contrastive Predictive Coding

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