Perceptual Reality Transformer: What Must an Illustration Preserve?

arXiv:2508.09852 · q-bio.NC, cs.AI, cs.CV, cs.NE · Submitted 2025-08-13 · Read on arXiv

q-bio.NC, cs.AI, cs.CV, cs.NE

Submitted: 2025-08-13

Updated: 2026-09-12

Code: https://github.com/linlab/prt

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

Terminology

• Transformer — 19× in this paper · explained in 2 episodes
• Embeddings — 12× in this paper · explained in 2 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Perceptual Reality Transformer — 6× in this paper
• Transformer Architecture — 5× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Attention Mechanism — 3× in this paper · explained in 3 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Latent Space — 2× in this paper · explained in 7 episodes
• AUROC — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Cross-Attention Mechanism — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Auto-Encoding Variational Bayes

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