The Domain Is a Residue: Adapting Self-Supervised Features, Not Generators

arXiv:2609.37330 · cs.CV, cs.GR, cs.LG · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.GR, cs.LG

Submitted: 2026-09-29

Updated: 2026-09-29

Project page: https://d3ixi.github.io/RepresentationFeatureAdapter/ABSTRACT

Terminology

• Transformer — 7× in this paper · explained in 2 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Consistency — 4× in this paper · explained in 3 episodes
• Decode — 4× in this paper · explained in 2 episodes
• Diffusion Model — 4× in this paper · explained in 8 episodes
• World Model — 4× in this paper · explained in 7 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Distillation — 3× in this paper · explained in 2 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Representation Feature Adapter — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Consistency Distillation — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• In IEEE Intelligent Vehicles Symposium — 1× in this paper
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Two-Stage Framework — 1× in this paper · explained in 2 episodes

Sources

• One Layer Is Enough: Adapting Pretrained Visual Encoders for Image Generation
• Multiplayer Interactive World Models with Representation Autoencoders
• AdapterTune: Zero-Initialized Low-Rank Adapters for Frozen Vision Transformers
• Unpaired Image-to-Image Translation via a Self-Supervised Semantic Bridge
• PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion
• Cyclone: Diffusion Model for Cycle-Consistent Weather Editing from Unpaired Driving Data
• Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models
• Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control
• X-Restormer++: 1st Place Solution for the UG2+ CVPR 2026 All-Weather Restoration Challenge
• One-Step Image Translation with Text-to-Image Models
• HyPER-GAN: Hybrid Patch-Based Image-to-Image Translation for Real-Time Photorealism Enhancement in Game Engines
• UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic Models
• DINOv3
• Improved Baselines with Representation Autoencoders
• UVCGAN v2: An Improved Cycle-Consistent GAN for Unpaired Image-to-Image Translation
• PRISM: Distribution-Gated Flow Matching for Controllable Unpaired Image Translation

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