DMA squared: Pixel-space Distribution Matching with Adversarial and Anchor Losses

arXiv:2609.38156 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Distillation — 15× in this paper · explained in 2 episodes
• Distribution Matching — 14× in this paper · explained in 2 episodes
• Alignment — 13× in this paper · explained in 1 episode
• Ablation — 10× in this paper · explained in 1 episode
• Diffusion Model — 7× in this paper · explained in 8 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• Diffusion Models — 5× in this paper · explained in 6 episodes
• AUC — 3× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Consistency — 2× in this paper · explained in 3 episodes
• Decoupling — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• In International Conference on Machine Learning — 1× in this paper
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset
• Generative Modeling via Drifting
• Representation Distribution Matching for One-Step Visual Generation
• PixWorld: Unifying 3D Scene Generation and Reconstruction in Pixel Space
• ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment
• GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual Generation
• PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion
• Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference
• DINOv2: Learning Robust Visual Features without Supervision
• PointDiT: Pixel-Space Diffusion for Monocular Geometry Estimation
• Representation Fr'echet Loss for Visual Generation
• PXDepth: Pixel-Space Modeling for Structure Preserving Monocular Depth Estimation

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