2 citations · 4 across the 5 of their papers we have counts for
6 papers
Quality-Aware Modulation for Diffusion Transformers
Luke Budny, Yuhong Guo, Kevin Cheung
Modern text-to-image diffusion models, such as diffusion transformers (DiT), rely on timestep or prompt embeddings to modulate the strength of the denoising process in each timeste…
Reliability-Guided Adaptive Ensembling for Robust Test-Time Adaptation
Adam Koziak, Yuhong Guo
Test-time adaptation (TTA) can mitigate domain shift without source data, but it is highly brittle under adversarially contaminated test streams, where corrupted inputs also destab…
Bi-Level Optimization for Single Domain Generalization
Marzi Heidari, Hanping Zhang, Hao Yan +1
Generalizing from a single labeled source domain to unseen target domains, without access to any target data during training, remains a fundamental challenge in robust machine lear…
Context-Aware Self-Adaptation for Domain Generalization
Hao Yan, Yuhong Guo
Domain generalization aims at developing suitable learning algorithms in source training domains such that the model learned can generalize well on a different unseen testing domai…
Lightweight Unsupervised Federated Learning with Pretrained Vision Language Model
Hao Yan, Yuhong Guo
Federated learning aims to tackle the ``isolated data island" problem, where it trains a collective model from physically isolated clients while safeguarding the privacy of users'…
Dual Moving Average Pseudo-Labeling for Source-Free Inductive Domain Adaptation
Hao Yan, Yuhong Guo
Unsupervised domain adaptation reduces the reliance on data annotation in deep learning by adapting knowledge from a source to a target domain. For privacy and efficiency concerns,…