105 citations · 433 across the 42 of their papers we have counts for
14 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…
EMO: Episodic Memory Optimization for Few-Shot Meta-Learning
Yingjun Du, Jiayi Shen, Xiantong Zhen +1
Few-shot meta-learning presents a challenge for gradient descent optimization due to the limited number of training samples per task. To address this issue, we propose an episodic…
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks
Wenfang Sun, Yingjun Du, Xiantong Zhen +3
Meta-learning algorithms are able to learn a new task using previously learned knowledge, but they often require a large number of meta-training tasks which may not be readily avai…
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…
Variational Model Perturbation for Source-Free Domain Adaptation
Mengmeng Jing, Xiantong Zhen, Jingjing Li +1
We aim for source-free domain adaptation, where the task is to deploy a model pre-trained on source domains to target domains. The challenges stem from the distribution shift from…
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…
Generative Kernel Continual learning
Mohammad Mahdi Derakhshani, Xiantong Zhen, Ling Shao +1
Kernel continual learning by \citet{derakhshani2021kernel} has recently emerged as a strong continual learner due to its non-parametric ability to tackle task interference and cata…
Multi-Task Neural Processes
Jiayi Shen, Xiantong Zhen, Marcel Worring +1
Neural processes have recently emerged as a class of powerful neural latent variable models that combine the strengths of neural networks and stochastic processes. As they can enco…
Hierarchical Variational Memory for Few-shot Learning Across Domains
Yingjun Du, Xiantong Zhen, Ling Shao +1
Neural memory enables fast adaptation to new tasks with just a few training samples. Existing memory models store features only from the single last layer, which does not generaliz…
Kernel Continual Learning
Mohammad Mahdi Derakhshani, Xiantong Zhen, Ling Shao +1
This paper introduces kernel continual learning, a simple but effective variant of continual learning that leverages the non-parametric nature of kernel methods to tackle catastrop…
MetaKernel: Learning Variational Random Features with Limited Labels
Yingjun Du, Haoliang Sun, Xiantong Zhen +4
Few-shot learning deals with the fundamental and challenging problem of learning from a few annotated samples, while being able to generalize well on new tasks. The crux of few-sho…
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…
Learning to Learn Variational Semantic Memory
Xiantong Zhen, Yingjun Du, Huan Xiong +3
In this paper, we introduce variational semantic memory into meta-learning to acquire long-term knowledge for few-shot learning. The variational semantic memory accrues and stores…
The Structure Transfer Machine Theory and Applications
Baochang Zhang, Lian Zhuo, Ze Wang +2
Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown. We propose a new representation learning method, termed Struc…