Representation by Design in Generation: Cross-View Class-Token Alignment in Diffusion Transformers

arXiv:2609.36348 · cs.CV, cs.AI, cs.LG · Submitted 2026-09-28 · Read on arXiv

cs.CV, cs.AI, cs.LG

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Alignment — 41× in this paper · explained in 1 episode
• Transformer — 22× in this paper · explained in 2 episodes
• Accuracy — 15× in this paper · explained in 1 episode
• Diffusion Model — 15× in this paper · explained in 8 episodes
• Diffusion Models — 12× in this paper · explained in 6 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Linear Probing — 8× in this paper · explained in 2 episodes
• Representation Learning — 7× in this paper · explained in 2 episodes
• Distillation — 5× in this paper · explained in 2 episodes
• Flow Matching — 5× in this paper · explained in 7 episodes
• Linear probe — 4× in this paper · explained in 2 episodes
• Self-distillation — 4× in this paper · explained in 6 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Entanglement — 1× in this paper · explained in 3 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes

Sources

• Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis
• Probing Diffusion Denoising Dynamics for Contrastive Representation Learning
• Conditioning Residuals for Diffusion Models via Representation Feedback
• iBOT: Image BERT Pre-Training with Online Tokenizer

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