Adapts Flow Matching from generative AI to probabilistic inversion, evaluated on a simple 2D velocity model and the OpenFWI seismic dataset.
W¨urstchen: An efficient architecture for large-scale text-to-image diffusion models.arXiv preprint arXiv:2306.00637
3 Pith papers cite this work, alongside 4 external citations. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Semi-DPO applies semi-supervised learning to noisy preference data in diffusion DPO by training first on consensus pairs then iteratively pseudo-labeling conflicts, yielding state-of-the-art alignment with complex human preferences.
WMGen-v1 generates diverse long-tail spatial images from one reference image via LVLM scene parsing, LLM-guided expansion, and diffusion synthesis, with detectors trained only on the synthetic data approaching real-data performance on benchmarks.
citing papers explorer
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Probabilistic Inversion with Flow Matching
Adapts Flow Matching from generative AI to probabilistic inversion, evaluated on a simple 2D velocity model and the OpenFWI seismic dataset.
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Learning from Noisy Preferences: A Semi-Supervised Learning Approach to Direct Preference Optimization
Semi-DPO applies semi-supervised learning to noisy preference data in diffusion DPO by training first on consensus pairs then iteratively pseudo-labeling conflicts, yielding state-of-the-art alignment with complex human preferences.
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One Image is All You Need: Agentic One-Shot Image Generation via Text-Based World Models for Long-Tail Spatial Perception
WMGen-v1 generates diverse long-tail spatial images from one reference image via LVLM scene parsing, LLM-guided expansion, and diffusion synthesis, with detectors trained only on the synthetic data approaching real-data performance on benchmarks.