Anomaly-Preference Image Generation

arXiv:2605.02439 · cs.CV, cs.LG · Submitted 2026-05-04 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-05-04

Updated: 2026-09-11

Comments: Accepted by ICML 2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Alignment — 27× in this paper · explained in 1 episode
• alpha — 27× in this paper · explained in 1 episode
• Fidelity — 15× in this paper · explained in 2 episodes
• Adaptation — 10× in this paper · explained in 1 episode
• Fine-Tuning — 10× in this paper · explained in 5 episodes
• Diffusion Model — 9× in this paper · explained in 8 episodes
• Direct Preference Optimization — 9× in this paper
• AUC — 8× in this paper · explained in 2 episodes
• Reinforcement Learning — 8× in this paper · explained in 4 episodes
• Regularization — 7× in this paper · explained in 3 episodes
• Time-Aware Capacity Allocation — 7× in this paper
• Consistency — 6× in this paper · explained in 3 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Ablation study — 5× in this paper · explained in 1 episode
• Accuracy — 5× in this paper · explained in 1 episode
• Anomaly Preference Optimization — 5× in this paper
• Diffusion Models — 5× in this paper · explained in 6 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Direct Preference Optimization (DPO) — 3× in this paper · explained in 9 episodes
• Structural Integrity — 3× in this paper · explained in 3 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Dual-Stream Architecture — 2× in this paper · explained in 3 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Reward Function — 2× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Average Precision — 1× in this paper
• Calibration — 1× in this paper · explained in 7 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Decoupled Learning — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Inception Score — 1× in this paper
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Structural Control — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuning
• ORPO: Monolithic Preference Optimization without Reference Model
• Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
• Constitutional AI: Harmlessness from AI Feedback
• Adam: A Method for Stochastic Optimization
• Aligning Text-to-Image Models using Human Feedback
• Explainable Deep Few-shot Anomaly Detection with Deviation Networks
• SLiC-HF: Sequence Likelihood Calibration with Human Feedback
• Denoising Diffusion Implicit Models

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