78 citations · 106 across the 8 of their papers we have counts for
5 papers · 1 filter
Probabilistic Test-Time Generalization by Variational Neighbor-Labeling
Sameer Ambekar, Zehao Xiao, Jiayi Shen +2
This paper strives for domain generalization, where models are trained exclusively on source domains before being deployed on unseen target domains. We follow the strict separation…
ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion
Yingjun Du, Zehao Xiao, Shengcai Liao +1
Prototype-based meta-learning has emerged as a powerful technique for addressing few-shot learning challenges. However, estimating a deterministic prototype using a simple average…
Energy-Based Test Sample Adaptation for Domain Generalization
Zehao Xiao, Xiantong Zhen, Shengcai Liao +1
In this paper, we propose energy-based sample adaptation at test time for domain generalization. Where previous works adapt their models to target domains, we adapt the unseen targ…
Learning to Generalize across Domains on Single Test Samples
Zehao Xiao, Xiantong Zhen, Ling Shao +1
We strive to learn a model from a set of source domains that generalizes well to unseen target domains. The main challenge in such a domain generalization scenario is the unavailab…
A Bit More Bayesian: Domain-Invariant Learning with Uncertainty
Zehao Xiao, Jiayi Shen, Xiantong Zhen +2
Domain generalization is challenging due to the domain shift and the uncertainty caused by the inaccessibility of target domain data. In this paper, we address both challenges with…