Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

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extinguish

Bernadette Andreosso-O’Callaghan with a surrogate predictor, that iteratively learns to generate samples from increasingly promising latent subspaces. This approach leads to very effective and efficient architecture search, while keeping the query amount low. In addition, our approach allows in a straightforward manner to jointly

oracle

PAEAN

https://doi.org/10.1057/9781137348463ges and very large point clouds, and demonstrate that it requires fewer than 25% of the parameters, 33% of the memory footprint, and 10% of the computation time of competing techniques such as ACORN to reach the same representation accuracy. A fast implementation of MINER for images and 3D volumes i

Ophthalmoscope

,Accelerating Score-Based Generative Models with?Preconditioned Diffusion Sampling,iversity validate that PDS consistently accelerates off-the-shelf SGMs whilst maintaining the synthesis quality. In particular, PDS can accelerate by up to . on more challenging high resolution (1024.1024) image generation.

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Overthrow

,Diverse Image Inpainting with?Normalizing Flow,ults. We propose Flow-Fill, a novel two-stage image inpainting framework that utilizes a conditional normalizing flow model to generate diverse structural priors in the first stage. Flow-Fill can directly estimate the joint probability density of the missing regions as a flow-based model without rea

HAIL

,TREND: Truncated Generalized Normal Density Estimation of?Inception Embeddings for?GAN Evaluation,on, which consequently eliminates the risk of faulty evaluation results. Furthermore, the proposed metric significantly improves robustness of evaluation results against variation of the number of image samples.

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