Search Results for author:
Found 10 papers, 7 papers with code
HyperPlanes: Hypernetwork Approach to Rapid NeRF Adaptation
Neural radiance fields (NeRFs) are a widely accepted standard for synthesizing new 3D object views from a small number of base images.
Bounding Evidence and Estimating Log-Likelihood in VAE
Many crucial problems in deep learning and statistics are caused by a variational gap, i. e., a difference between evidence and evidence lower bound (ELBO).
Target Layer Regularization for Continual Learning Using Cramer-Wold Generator
We propose an effective regularization strategy (CW-TaLaR) for solving continual learning problems.
HyperCube: Implicit Field Representations of Voxelized 3D Models
Recently introduced implicit field representations offer an effective way of generating 3D object shapes.
HyperPocket: Generative Point Cloud Completion
In this work, we reformulate the problem of point cloud completion into an object hallucination task.
Modeling 3D Surface Manifolds with a Locally Conditioned Atlas
This way, we can sample a mesh quad on that sphere and project it back onto the object's manifold.
Generative models with kernel distance in data space
First, an autoencoder based architecture, using kernel measures, is built to model a manifold of data.
Gravilon: Applications of a New Gradient Descent Method to Machine Learning
These methods modify the effect of the gradient in updating the values of the parameters.
Sliced generative models
In this paper we discuss a class of AutoEncoder based generative models based on one dimensional sliced approach.
Cramer-Wold AutoEncoder
The crucial new ingredient is the introduction of a new (Cramer-Wold) metric in the space of densities, which replaces the Wasserstein metric used in SWAE.
