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Found 11 papers, 7 papers with code
Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections
This manifold lens provides both clarity as to why some DGMs (e. g. diffusion models and some generative adversarial networks) empirically surpass others (e. g. likelihood-based models such as variational autoencoders, normalizing flows, or energy-based models) at sample generation, and guidance for devising more performant DGMs.
A Geometric Explanation of the Likelihood OOD Detection Paradox
We also show that this scenario can be identified through local intrinsic dimension (LID) estimation, and propose a method for OOD detection which pairs the likelihoods and LID estimates obtained from a pre-trained DGM.
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
Comparing to 17 modern metrics for evaluating the overall performance, fidelity, diversity, rarity, and memorization of generative models, we find that the state-of-the-art perceptual realism of diffusion models as judged by humans is not reflected in commonly reported metrics such as FID.
Denoising Deep Generative Models
Likelihood-based deep generative models have recently been shown to exhibit pathological behaviour under the manifold hypothesis as a consequence of using high-dimensional densities to model data with low-dimensional structure.
CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
Precision measurements and new physics searches at the Large Hadron Collider require efficient simulations of particle propagation and interactions within the detectors.
Find Your Friends: Personalized Federated Learning with the Right Collaborators
In the traditional federated learning setting, a central server coordinates a network of clients to train one global model.
Verifying the Union of Manifolds Hypothesis for Image Data
Assuming that data lies on a single manifold implies intrinsic dimension is identical across the entire data space, and does not allow for subregions of this space to have a different number of factors of variation.
Neural Implicit Manifold Learning for Topology-Aware Density Estimation
We then learn the probability density within $\mathcal{M}$ with a constrained energy-based model, which employs a constrained variant of Langevin dynamics to train and sample from the learned manifold.
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models
We propose a class of two-step procedures consisting of a dimensionality reduction step followed by maximum-likelihood density estimation, and prove that they recover the data-generating distribution in the nonparametric regime, thus avoiding manifold overfitting.
Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows
Normalizing flows are generative models that provide tractable density estimation via an invertible transformation from a simple base distribution to a complex target distribution.
Conformal Embedding Flows: Tractable Density Estimation on Learned Manifolds
Normalizing flows are generative models that provide tractable density estimation by transforming a simple distribution into a complex one.
