← Back to all news
arXiv Machine Learning By Ruchi Sandilya, Sumaira Perez, Charles Lynch, Lindsay Victoria, Benjamin Zebley, Derrick Matthew Buchanan, Mahendra T. Bhati, Nolan Williams, Timothy J. Spellman, Faith M. Gunning, Conor Liston, Logan Grosenick

Contrastive Diffusion Alignment: Learning Structured Latents for Controllable Generation

Read the original on arXiv Machine Learning →

arXiv:2510. 14190v3 Announce Type: replace Abstract: Diffusion models excel at generation, but their latent spaces are high dimensional and not explicitly organized for interpretation or control.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Computer Vision
Sep 23

Latent Dataset Distillation for Human Motion Prediction

The paper introduces a latent dataset distillation framework for human motion prediction, addressing the limitations of traditional gradient matching by incorporating a learned motion prior. Motions are compressed using a residual‑quantized variational autoencoder, and distillation updates only a latent bank while keeping the decoder frozen, ensuring synthetic motions remain plausible. Experiments on Human3.6M, CMU, and 3DPW datasets demonstrate that this method outperforms direct gradient matching in most settings and yields more realistic synthetic motions.

By Ge Tian, Guang Li, Takahiro Ogawa, Miki Haseyama
efficiency
More like this →