Search Results for author:
Found 62 papers, 46 papers with code
CEC-MMR: Cross-Entropy Clustering Approach to Multi-Modal Regression
In this paper, we introduce CEC-MMR, a novel approach based on Cross-Entropy Clustering (CEC), which allows for the automatic detection of the number of components in a regression problem.
Classifier-free Guidance with Adaptive Scaling
In practice, in controlling the impact of guidance we can see the trade-off between the quality of the generated images and correspondence to the prompt.
MeshSplats: Mesh-Based Rendering with Gaussian Splatting Initialization
GS-based algorithms almost always bypass classical methods such as ray tracing, which offers numerous inherent advantages for rendering.
RaySplats: Ray Tracing based Gaussian Splatting
3D Gaussian Splatting (3DGS) is a process that enables the direct creation of 3D objects from 2D images.
Neural Surface Priors for Editable Gaussian Splatting
Unlike other methods, our pipeline allows modifications applied to the extracted mesh to be propagated to the proxy representation, from which we recover the updated parameters of the Gaussians.
PR-ENDO: Physically Based Relightable Gaussian Splatting for Endoscopy
Endoscopic procedures are crucial for colorectal cancer diagnosis, and three-dimensional reconstruction of the environment for real-time novel-view synthesis can significantly enhance diagnosis.
VeGaS: Video Gaussian Splatting
One potential solution is to use a 3D Gaussian Splatting (3DGS) based model, such as the Video Gaussian Representation (VGR), which is capable of encoding video as a multitude of 3D Gaussians and is applicable for numerous video processing operations, including editing.
FreSh: Frequency Shifting for Accelerated Neural Representation Learning
Implicit Neural Representations (INRs) have recently gained attention as a powerful approach for continuously representing signals such as images, videos, and 3D shapes using multilayer perceptrons (MLPs).
Make Interval Bound Propagation great again
Two crucial problems in NNC are of profound interest to the scientific community: how to calculate the robustness of a given pre-trained network and how to construct robust networks.
AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion Models
To solve this issue, we introduce AutoLoRA, a novel guidance technique for diffusion models fine-tuned with the LoRA approach.
MiraGe: Editable 2D Images using Gaussian Splatting
Our approach improves the rendering quality and allows realistic image modifications, including human-inspired perception of photos in the 3D world.
GASP: Gaussian Splatting for Physic-Based Simulations
In contrast, our Gaussian Splatting for Physics-Based Simulations (GASP) model uses such a map (without any modifications) and flat Gaussian distributions, which are parameterized by three points (mesh faces).
GeoGuide: Geometric guidance of diffusion models
Although ADM-G successfully generates elements from the given class, there is a significant quality gap compared to a model originally conditioned on this class.
HINT: Hypernetwork Approach to Training Weight Interval Regions in Continual Learning
To address this issue, we introduce HINT, a technique that employs interval arithmetic within the embedding space and utilizes a hypernetwork to map these intervals to the target network parameter space.
D-MiSo: Editing Dynamic 3D Scenes using Multi-Gaussians Soup
Thus, we can make the scene's dynamic editable over time or while maintaining partial dynamics.
Deepfake for the Good: Generating Avatars through Face-Swapping with Implicit Deepfake Generation
NeRFs encode the object's shape and color in neural network weights using a handful of images with known camera positions to generate novel views.
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.
GaMeS: Mesh-Based Adapting and Modification of Gaussian Splatting
Gaussian Splatting (GS) is a novel, state-of-the-art technique for rendering points in a 3D scene by approximating their contribution to image pixels through Gaussian distributions, warranting fast training and real-time rendering.
Gaussian Splatting with NeRF-based Color and Opacity
Our model uses Gaussian distributions with trainable positions (i. e. means of Gaussian), shape (i. e. covariance of Gaussian), color and opacity, and a neural network that takes Gaussian parameters and viewing direction to produce changes in the said color and opacity.
MeVGAN: GAN-based Plugin Model for Video Generation with Applications in Colonoscopy
However, video generation of high-resolution data is a very demanding task for generative models, due to the large need for memory.
HyperMask: Adaptive Hypernetwork-based Masks for Continual Learning
In the paper, we propose a method called HyperMask, which dynamically filters a target network depending on the CL task.
Face Identity-Aware Disentanglement in StyleGAN
Conditional GANs are frequently used for manipulating the attributes of face images, such as expression, hairstyle, pose, or age.
MultiPlaneNeRF: Neural Radiance Field with Non-Trainable Representation
Furthermore, we can train MultiPlaneNeRF on a large data set and force our implicit decoder to generalize across many objects.
Gaussian model for closed curves
In this work, we propose a density representation of the closed curve, which can be used to detect the complicated templates in the data.
NeRFlame: FLAME-based conditioning of NeRF for 3D face rendering
In contrast to traditional NeRF-based structures that use neural networks for RGB color and volume density modeling, our approach utilizes the FLAME mesh as a distinct density volume.
Hypernetworks build Implicit Neural Representations of Sounds
Implicit Neural Representations (INRs) are nowadays used to represent multimedia signals across various real-life applications, including image super-resolution, image compression, or 3D rendering.
HyperNeRFGAN: Hypernetwork approach to 3D NeRF GAN
The recent surge in popularity of deep generative models for 3D objects has highlighted the need for more efficient training methods, particularly given the difficulties associated with training with conventional 3D representations, such as voxels or point clouds.
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks
Implicit neural representations (INRs) are a rapidly growing research field, which provides alternative ways to represent multimedia signals.
Hypernetwork approach to Bayesian MAML
Unfortunately, previous modifications of MAML are limited due to the simplicity of Gaussian posteriors, MAML-like gradient-based weight updates, or by the same structure enforced for universal and adapted weights.
LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood
We attempt to address that challenge by proposing a novel approach to the problem: Local Intrinsic Dimension estimation using approximate Likelihood (LIDL).
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).
Continual Learning with Guarantees via Weight Interval Constraints
We introduce a new training paradigm that enforces interval constraints on neural network parameter space to control forgetting.
Points2NeRF: Generating Neural Radiance Fields from 3D point cloud
Contemporary registration devices for 3D visual information, such as LIDARs and various depth cameras, capture data as 3D point clouds.
HyperShot: Few-Shot Learning by Kernel HyperNetworks
Few-shot models aim at making predictions using a minimal number of labeled examples from a given task.
Target Layer Regularization for Continual Learning Using Cramer-Wold Generator
We propose an effective regularization strategy (CW-TaLaR) for solving continual learning problems.
Non-Gaussian Gaussian Processes for Few-Shot Regression
This makes the GP posterior locally non-Gaussian, therefore we name our method Non-Gaussian Gaussian Processes (NGGPs).
HyperCube: Implicit Field Representations of Voxelized 3D Models
Recently introduced implicit field representations offer an effective way of generating 3D object shapes.
Efficient GPU implementation of randomized SVD and its applications
In this work, we leverage efficient processing operations that can be run in parallel on modern Graphical Processing Units (GPUs), predominant computing architecture used e. g. in deep learning, to reduce the computational burden of computing matrix decompositions.
Flow-based SVDD for anomaly detection
We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools.
HyperColor: A HyperNetwork Approach for Synthesizing Auto-colored 3D Models for Game Scenes Population
In such a case, we have to ``understand'' the object's composition and coloring scheme of each part.
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.
HyperPocket: Generative Point Cloud Completion
In this work, we reformulate the problem of point cloud completion into an object hallucination task.
RegFlow: Probabilistic Flow-based Regression for Future Prediction
Predicting future states or actions of a given system remains a fundamental, yet unsolved challenge of intelligence, especially in the scope of complex and non-deterministic scenarios, such as modeling behavior of humans.
Processing of incomplete images by (graph) convolutional neural networks
We investigate the problem of training neural networks from incomplete images without replacing missing values.
OneFlow: One-class flow for anomaly detection based on a minimal volume region
We propose OneFlow - a flow-based one-class classifier for anomaly (outlier) detection that finds a minimal volume bounding region.
Generative models with kernel distance in data space
First, an autoencoder based architecture, using kernel measures, is built to model a manifold of data.
Adversarial Examples Detection and Analysis with Layer-wise Autoencoders
We present a mechanism for detecting adversarial examples based on data representations taken from the hidden layers of the target network.
HyperFlow: Representing 3D Objects as Surfaces
To that end, we devise a generative model that uses a hypernetwork to return the weights of a Continuous Normalizing Flows (CNF) target network.
LocoGAN -- Locally Convolutional GAN
In the paper we construct a fully convolutional GAN model: LocoGAN, which latent space is given by noise-like images of possibly different resolutions.
Hypernetwork approach to generating point clouds
The main idea of our HyperCloud method is to build a hyper network that returns weights of a particular neural network (target network) trained to map points from a uniform unit ball distribution into a 3D shape.
WICA: nonlinear weighted ICA
Independent Component Analysis (ICA) aims to find a coordinate system in which the components of the data are independent.
Spatial Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have recently become the primary choice for learning from graph-structured data, superseding hash fingerprints in representing chemical compounds.
Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models
We present an efficient technique, which allows to train classification networks which are verifiably robust against norm-bounded adversarial attacks.
Independent Component Analysis based on multiple data-weighting
Independent Component Analysis (ICA) - one of the basic tools in data analysis - aims to find a coordinate system in which the components of the data are independent.
Non-linear ICA based on Cramer-Wold metric
Non-linear source separation is a challenging open problem with many applications.
Sliced generative models
In this paper we discuss a class of AutoEncoder based generative models based on one dimensional sliced approach.
Set Aggregation Network as a Trainable Pooling Layer
Global pooling, such as max- or sum-pooling, is one of the key ingredients in deep neural networks used for processing images, texts, graphs and other types of structured data.
Deep processing of structured data
We construct a general unified framework for learning representation of structured data, i. e. data which cannot be represented as the fixed-length vectors (e. g. sets, graphs, texts or images of varying sizes).
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.
Processing of missing data by neural networks
We propose a general, theoretically justified mechanism for processing missing data by neural networks.
Introduction to Cross-Entropy Clustering The R Package CEC
The R Package CEC performs clustering based on the cross-entropy clustering (CEC) method, which was recently developed with the use of information theory.
