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Found 9 papers, 6 papers with code
Schrodinger Bridges Beat Diffusion Models on Text-to-Speech Synthesis
Specifically, we leverage the latent representation obtained from text input as our prior, and build a fully tractable Schrodinger bridge between it and the ground-truth mel-spectrogram, leading to a data-to-data process.
InstructPix2NeRF: Instructed 3D Portrait Editing from a Single Image
With the success of Neural Radiance Field (NeRF) in 3D-aware portrait editing, a variety of works have achieved promising results regarding both quality and 3D consistency.
DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model Statistics
In this work, we propose a novel formulation towards the optimal parameterization during sampling that minimizes the first-order discretization error of the ODE solution.
High-quality Image Dehazing with Diffusion Model
The latter stage exploits the strong generation ability of DDPM to compensate for the haze-induced huge information loss, by working in conjunction with the physical modelling.
Precheck Sequence Based False Base Station Detection During Handover: A Physical Layer Security Scheme
The source BS compares the uploaded signal power and shifts UE to another BS that can provide the strongest signal.
Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs
The probability flow ordinary differential equation (ODE) of diffusion models (i. e., diffusion ODEs) is a particular case of continuous normalizing flows (CNFs), which enables deterministic inference and exact likelihood evaluation.
Ranked #1 on
Image Generation
on ImageNet 32x32
(bpd metric)
PREIM3D: 3D Consistent Precise Image Attribute Editing from a Single Image
As for imprecise image editing, we attribute the problem to the gap between the latent space of real images and that of generated images.
Learning Sample Relationship for Exposure Correction
Exposure correction task aims to correct the underexposure and its adverse overexposure images to the normal exposure in a single network.
Maximum Likelihood Training for Score-Based Diffusion ODEs by High-Order Denoising Score Matching
To fill up this gap, we show that the negative likelihood of the ODE can be bounded by controlling the first, second, and third-order score matching errors; and we further present a novel high-order denoising score matching method to enable maximum likelihood training of score-based diffusion ODEs.
