FuseReg: Regularizing Layer Fusion Mitigates the Reconstruction-Generation Gap in Representation Autoencoders

arXiv:2609.31620 · cs.CV · Submitted 2026-09-25 · Read on arXiv

cs.CV

Submitted: 2026-09-25

Updated: 2026-09-28

Terminology

• Regularization — 26× in this paper · explained in 3 episodes
• Robustness — 19× in this paper · explained in 5 episodes
• Transformer — 15× in this paper · explained in 2 episodes
• Alignment — 12× in this paper · explained in 1 episode
• Diffusion Model — 7× in this paper · explained in 8 episodes
• Latent Space — 6× in this paper · explained in 7 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Latent Representation — 4× in this paper · explained in 2 episodes
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Inception Score — 2× in this paper
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Reconstruction Error — 2× in this paper · explained in 2 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Additivity — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Embeddings — 1× in this paper · explained in 2 episodes
• Exchangeability — 1× in this paper · explained in 2 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes

Sources

• Laminating Representation Autoencoders for Efficient Diffusion
• Hyperspherical Autoencoder for High-Fidelity Image Reconstruction and Generation
• AlignTok: Aligning Visual Foundation Encoders to Tokenizers for Diffusion Models
• Attentive multilayer fusion for vision transformers
• The Prism Hypothesis: Harmonizing Semantic and Pixel Representations via Unified Autoencoding
• RPiAE: A Representation-Pivoted Autoencoder Enhancing Both Image Generation and Editing
• V-RAE: Rethinking Video Latent Spaces for Generation
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• REPA-E: Unlocking VAE for End-to-End Tuning with Latent Diffusion Transformers
• Flow Matching for Generative Modeling
• Improving Reconstruction of Representation Autoencoder
• Representation Alignment for Just Image Transformers is not Easier than You Think
• What matters for Representation Alignment: Global Information or Spatial Structure?
• Improved Baselines with Representation Autoencoders
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• VideoRAE: Taming Video Foundation Models for Generative Modeling via Representation Autoencoders
• Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think
• The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
• Both Semantics and Reconstruction Matter: Making Representation Encoders Ready for Text-to-Image Generation and Editing
• Diffusion Transformers with Representation Autoencoders

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