Generative Drifting is Secretly Score Matching: a Spectral and Variational Perspective

Export BriefConnect with Author
View PDF ↗
PDF Viewer
Open Full PDF

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (30)

[1]
A Unified View of Drifting and Score-Based Models
2026Chieh-Hsin Lai, Bac Nguyen et al.
[2]
A Long-Short Flow-Map Perspective for Drifting Models
2026Zhiqi Li, Bo Zhu
[3]
Generative Modeling via Drifting
2026Mingyang Deng, He Li et al.
[4]
The Stein-log-Sobolev inequality and the exponential rate of convergence for the continuous Stein variational gradient descent method
2024José A. Carrillo, Jakub Skrzeczkowski et al.
[5]
The Score-Difference Flow for Implicit Generative Modeling
2023Romann M. Weber
[6]
Consistency Models
2023Yang Song, Prafulla Dhariwal et al.
[7]
Generative Adversarial Networks
2021I. Goodfellow, Jean Pouget-Abadie et al.
[8]
How to Train Your Energy-Based Models
2021Yang Song, Diederik P. Kingma
[9]
Score-Based Generative Modeling through Stochastic Differential Equations
2020Yang Song, Jascha Narain Sohl-Dickstein et al.
[10]
Exploring Simple Siamese Representation Learning
2020Xinlei Chen, Kaiming He
[11]
Denoising Diffusion Probabilistic Models
2020Jonathan Ho, Ajay Jain et al.
[12]
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
2020Matthew Tancik, Pratul P. Srinivasan et al.
[13]
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
2019Will Grathwohl, Kuan-Chieh Jackson Wang et al.
[14]
Generative Modeling by Estimating Gradients of the Data Distribution
2019Yang Song, Stefano Ermon
[15]
Implicit Generation and Modeling with Energy Based Models
2019Yilun Du, Igor Mordatch
[16]
Interpolating between Optimal Transport and MMD using Sinkhorn Divergences
2018Jean Feydy, Thibault Séjourné et al.
[17]
Large Scale GAN Training for High Fidelity Natural Image Synthesis
2018Andrew Brock, Jeff Donahue et al.
[18]
On the Spectral Bias of Neural Networks
2018Nasim Rahaman, A. Baratin et al.
[19]
Computational Optimal Transport
2018G. Peyré, Marco Cuturi
[20]
Stein Variational Gradient Descent as Gradient Flow
2017Qiang Liu

Showing 20 of 30 references

Founder's Pitch

"A theoretical framework for generative modeling via drifting that enhances understanding and performance of image generation."

Generative ModelingScore: 4View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

0/4 signals

0

Series A Potential

0/4 signals

0

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 3/10/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.

Related Papers

Loading…