MESA restores ancient inscription textures via multi-exemplar style transfer from VGG19 features with per-layer exemplar selection and OCR-derived weights, without any model training.
In: 2017 IEEE International Conference on Computer Vision (ICCV)
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Timestep embeddings are redundant in diffusion models under certain conditions, with time-agnostic variants matching or exceeding conditioned models on FID, precision, and recall for CelebA and CIFAR-10.
citing papers explorer
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MESA: A Training-Free Multi-Exemplar Deep Framework for Restoring Ancient Inscription Textures
MESA restores ancient inscription textures via multi-exemplar style transfer from VGG19 features with per-layer exemplar selection and OCR-derived weights, without any model training.
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On the Redundancy of Timestep Embeddings in Diffusion Models
Timestep embeddings are redundant in diffusion models under certain conditions, with time-agnostic variants matching or exceeding conditioned models on FID, precision, and recall for CelebA and CIFAR-10.