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energy distance

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Machine Learning Algorithms & Natural Language Processing

Aug 10, 2026 · Artificial Intelligence

Training One‑Step Generative Models Without CFG, DMD, GAN, Drifting, or MeanFlow

The paper introduces TBSM, a lightweight direction‑tracking network that learns fake‑to‑real movement fields from three‑sample scattering events, enabling single‑forward‑pass generation and achieving state‑of‑the‑art FID scores on ImageNet and a 20B text‑to‑image model without relying on CFG, DMD, GAN, drifting, or mean‑flow techniques.

PixelDiTQwen-ImageTBSM

0 likes · 12 min read

Training One‑Step Generative Models Without CFG, DMD, GAN, Drifting, or MeanFlow

Machine Heart

Aug 9, 2026 · Artificial Intelligence

How to Train a One‑Step Generative Model Without CFG, DMD, GAN, or Drifting

The paper introduces TBSM, a lightweight direction‑tracking network that learns per‑sample Fake‑to‑Real vectors to guide a generator, enabling single‑forward (NFE=1) image synthesis with FID 1.92 on ImageNet‑512 and high‑quality text‑to‑image results, all without CFG, DMD, GAN, or drifting methods.

TBSMdiffusion modelsdirection tracking

0 likes · 11 min read

How to Train a One‑Step Generative Model Without CFG, DMD, GAN, or Drifting

Data Party THU

Apr 6, 2026 · Fundamentals

How Energy Distance Detects Distribution Shifts Between Training and Test Sets

Energy Distance is a statistical metric that quantifies the separation between two probability distributions by comparing cross‑distribution and within‑distribution Euclidean distances, enabling detection of data drift, covariate shift, and other multivariate distribution changes, especially when combined with permutation testing for statistical significance.

data driftdistribution shiftenergy distance

0 likes · 7 min read

How Energy Distance Detects Distribution Shifts Between Training and Test Sets

DeepHub IMBA

Mar 6, 2026 · Fundamentals

Measuring Multivariate Distribution Differences with Energy Distance

Energy Distance is a statistical metric that quantifies how far two multivariate probability distributions diverge by comparing cross‑distribution and within‑distribution Euclidean distances, and it can be combined with permutation testing to assess the significance of observed shifts.

Distribution Comparisondata driftenergy distance

0 likes · 6 min read

Measuring Multivariate Distribution Differences with Energy Distance