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Found 19 papers, 12 papers with code
Diffusion Autoencoders for Few-shot Image Generation in Hyperbolic Space
However, existing methods often suffer from a trade-off between image quality and diversity while offering limited control over the attributes of newly generated images.
Infinite-Resolution Integral Noise Warping for Diffusion Models
Adapting pretrained image-based diffusion models to generate temporally consistent videos has become an impactful generative modeling research direction.
BIFRÖST: 3D-Aware Image compositing with Language Instructions
This paper introduces Bifr\"ost, a novel 3D-aware framework that is built upon diffusion models to perform instruction-based image composition.
radarODE: An ODE-Embedded Deep Learning Model for Contactless ECG Reconstruction from Millimeter-Wave Radar
Radar-based contactless cardiac monitoring has become a popular research direction recently, but the fine-grained electrocardiogram (ECG) signal is still hard to reconstruct from millimeter-wave radar signal.
Debiased Distribution Compression
Modern compression methods can summarize a target distribution $\mathbb{P}$ more succinctly than i. i. d.
DAIRstega: Dynamically Allocated Interval-Based Generative Linguistic Steganography with Roulette Wheel
Based on the invisible characteristics of LS, we give three constraints that need to be met to design the function.
UP4LS: User Profile Constructed by Multiple Attributes for Enhancing Linguistic Steganalysis
The extracted features are mapped to high-dimensional user features via the deep-learning model of the method to be improved.
Monitoring and Adapting ML Models on Mobile Devices
ML models are increasingly being pushed to mobile devices, for low-latency inference and offline operation.
Sampling with Mollified Interaction Energy Descent
These energies rely on mollifier functions -- smooth approximations of the Dirac delta originated from PDE theory.
Learning Proximal Operators to Discover Multiple Optima
We present an end-to-end method to learn the proximal operator of a family of training problems so that multiple local minima can be quickly obtained from initial guesses by iterating the learned operator, emulating the proximal-point algorithm that has fast convergence.
Wasserstein Iterative Networks for Barycenter Estimation
Wasserstein barycenters have become popular due to their ability to represent the average of probability measures in a geometrically meaningful way.
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark
Despite the recent popularity of neural network-based solvers for optimal transport (OT), there is no standard quantitative way to evaluate their performance.
Large-Scale Wasserstein Gradient Flows
Specifically, Fokker-Planck equations, which model the diffusion of probability measures, can be understood as gradient descent over entropy functionals in Wasserstein space.
Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization
Wasserstein barycenters provide a geometric notion of the weighted average of probability measures based on optimal transport.
Continuous Regularized Wasserstein Barycenters
Leveraging a new dual formulation for the regularized Wasserstein barycenter problem, we introduce a stochastic algorithm that constructs a continuous approximation of the barycenter.
Comparing Alternative Route Planning Techniques: A Comparative User Study on Melbourne, Dhaka and Copenhagen Road Networks
Motivated by this, in this paper, we present a user study conducted on the road networks of Melbourne, Dhaka and Copenhagen that compares the quality (as perceived by the users) of the alternative routes generated by four of the most popular existing approaches including the routes provided by Google Maps.
SentPWNet: A Unified Sentence Pair Weighting Network for Task-specific Sentence Embedding
Our model, SentPWNet, exploits the neighboring spatial distribution of each sentence as locality weight to indicate the informative level of sentence pair.
Supervised Fitting of Geometric Primitives to 3D Point Clouds
Fitting geometric primitives to 3D point cloud data bridges a gap between low-level digitized 3D data and high-level structural information on the underlying 3D shapes.
