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Found 8 papers, 2 papers with code
Variational Sampling of Temporal Trajectories
In this work, we introduce a mechanism to learn the distribution of trajectories by parameterizing the transition function $f$ explicitly as an element in a function space.
Image2Gif: Generating Continuous Realistic Animations with Warping NODEs
Generating smooth animations from a limited number of sequential observations has a number of applications in vision.
Understanding Uncertainty Maps in Vision With Statistical Testing
Quantitative descriptions of confidence intervals and uncertainties of the predictions of a model are needed in many applications in vision and machine learning.
Forward Operator Estimation in Generative Models with Kernel Transfer Operators
Generative models which use explicit density modeling (e. g., variational autoencoders, flow-based generative models) involve finding a mapping from a known distribution, e. g. Gaussian, to the unknown input distribution.
$(\textrm{Implicit})^2$: Implicit Layers for Implicit Representations
Recent research in deep learning has investigated two very different forms of ''implicitness'': implicit representations model high-frequency data such as images or 3D shapes directly via a low-dimensional neural network (often using e. g., sinusoidal bases or nonlinearities); implicit layers, in contrast, refer to techniques where the forward pass of a network is computed via non-linear dynamical systems, such as fixed-point or differential equation solutions, with the backward pass computed via the implicit function theorem.
Distribution Matching in Deep Generative Models with Kernel Transfer Operators
Generative models which use explicit density modeling (e. g., variational autoencoders, flow-based generative models) involve finding a mapping from a known distribution, e. g. Gaussian, to the unknown input distribution.
Can Kernel Transfer Operators Help Flow based Generative Models?
Flow-based generative models refer to deep generative models with tractable likelihoods, and offer several attractive properties including efficient density estimation and sampling.
DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer
Motivated by developments in modality transfer in vision, we study the generation of certain types of PET images from MRI data.
