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pro vyhledávání: '"Pock, Thomas"'
Medical image segmentation is a crucial task that relies on the ability to accurately identify and isolate regions of interest in medical images. Thereby, generative approaches allow to capture the statistical properties of segmentation masks that ar
Externí odkaz:
http://arxiv.org/abs/2405.18087
Selective, Interpretable, and Motion Consistent Privacy Attribute Obfuscation for Action Recognition
Concerns for the privacy of individuals captured in public imagery have led to privacy-preserving action recognition. Existing approaches often suffer from issues arising through obfuscation being applied globally and a lack of interpretability. Glob
Externí odkaz:
http://arxiv.org/abs/2403.12710
We present a novel diffusion-based approach to generate synthetic histopathological Whole Slide Images (WSIs) at an unprecedented gigapixel scale. Synthetic WSIs have many potential applications: They can augment training datasets to enhance the perf
Externí odkaz:
http://arxiv.org/abs/2311.08199
In this work we tackle the problem of estimating the density $ f_X $ of a random variable $ X $ by successive smoothing, such that the smoothed random variable $ Y $ fulfills the diffusion partial differential equation $ (\partial_t - \Delta_1)f_Y(\,
Externí odkaz:
http://arxiv.org/abs/2310.12653
This paper is concerned with sampling from probability distributions $\pi$ on $\mathbb{R}^d$ admitting a density of the form $\pi(x) \propto e^{-U(x)}$, where $U(x)=F(x)+G(Kx)$ with $K$ being a linear operator and $G$ being non-differentiable. Two di
Externí odkaz:
http://arxiv.org/abs/2308.01417
We examine the assumption that the hidden-state vectors of recurrent neural networks (RNNs) tend to form clusters of semantically similar vectors, which we dub the clustering hypothesis. While this hypothesis has been assumed in the analysis of RNNs
Externí odkaz:
http://arxiv.org/abs/2306.16854
We study the problem of approximate sampling from non-log-concave distributions, e.g., Gaussian mixtures, which is often challenging even in low dimensions due to their multimodality. We focus on performing this task via Markov chain Monte Carlo (MCM
Externí odkaz:
http://arxiv.org/abs/2305.15988
The total generalized variation extends the total variation by incorporating higher-order smoothness. Thus, it can also suffer from similar discretization issues related to isotropy. Inspired by the success of novel discretization schemes of the tota
Externí odkaz:
http://arxiv.org/abs/2303.09349
Medical image segmentation is a crucial task that relies on the ability to accurately identify and isolate regions of interest in medical images. Thereby, generative approaches allow to capture the statistical properties of segmentation masks that ar
Externí odkaz:
http://arxiv.org/abs/2303.05966
Autor:
Kobler, Erich, Pock, Thomas
In this paper, we propose a unified framework of denoising score-based models in the context of graduated non-convex energy minimization. We show that for sufficiently large noise variance, the associated negative log density -- the energy -- becomes
Externí odkaz:
http://arxiv.org/abs/2302.10502