52 citations · 83 across the 12 of their papers we have counts for
6 papers · 1 filter
Mean Flows for One-step Generative Modeling
Zhengyang Geng, Mingyang Deng, Xingjian Bai +2
We propose a principled and effective framework for one-step generative modeling. We introduce the notion of average velocity to characterize flow fields, in contrast to instantane…
Consistency Models Made Easy
Zhengyang Geng, Ashwini Pokle, William Luo +2
Consistency models (CMs) offer faster sampling than traditional diffusion models, but their training is resource-intensive. For example, as of 2024, training a state-of-the-art CM…
TorchDEQ: A Library for Deep Equilibrium Models
Zhengyang Geng, J. Zico Kolter
Deep Equilibrium (DEQ) Models, an emerging class of implicit models that maps inputs to fixed points of neural networks, are of growing interest in the deep learning community. How…
Deep Equilibrium Approaches to Diffusion Models
Ashwini Pokle, Zhengyang Geng, Zico Kolter
Diffusion-based generative models are extremely effective in generating high-quality images, with generated samples often surpassing the quality of those produced by other models u…
On Training Implicit Models
Zhengyang Geng, Xin-Yu Zhang, Shaojie Bai +2
This paper focuses on training implicit models of infinite layers. Specifically, previous works employ implicit differentiation and solve the exact gradient for the backward propag…
Residual Relaxation for Multi-view Representation Learning
Yifei Wang, Zhengyang Geng, Feng Jiang +4
Multi-view methods learn representations by aligning multiple views of the same image and their performance largely depends on the choice of data augmentation. In this paper, we no…