Publications

Hierarchical Variational Memory for Few-shot Learning Across Domains
ICLR 2022
We propose a hierarchical memory that stores features at different semantic levels for the cross-domain few-shot learning.
Yingjun Du, Xiantong Zhen, Ling Shao, Cees Snoek
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Hierarchical Variational Memory for Few-shot Learning Across Domains
MetaKernel: Learning Variational Random Features with Limited Labels
TPAMI
We propose meta-learning kernels with random Fourier features for few-shot learning.
Yingjun Du, Haoliang Sun, Xiantong Zhen, Jun Xu, Yilong Yin, Ling Shao, Cees Snoek
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MetaKernel: Learning Variational Random Features with Limited Labels
Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation
ACL 2021
We propose a model of semantic memory for word sense disambiguation in a meta-learning setting.
Yingjun Du, Nithin Holla, Xiantong Zhen, Cees Snoek Ekaterina Shutova,
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Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation
MetaNorm: Learning to Normalize Few-shot Batches Across Domains
ICLR 2021
We propose MetaNorm, a simple yet effective meta-learning normalization. It tackles the aforementioned issues in a unified way by leveraging the meta-learning setting and learns to infer adaptive statistics for batch normalization.
Yingjun Du, Xiantong Zhen, Ling Shao, Cees Snoek
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MetaNorm: Learning to Normalize Few-shot Batches Across Domains
Learning to Learn Variational Semantic Memory
NeurIPS 2020
We introduce variational semantic memory into meta-learning to acquire long-term knowledge for few-shot learning.
Xiantong Zhen*, Yingjun Du*, Cees Snoek, Ling Shao
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Learning to Learn Variational Semantic Memory
Learning to Learn Kernels with Variational Random Features
ICML 2020
We introduce kernels with random Fourier features in the meta-learning framework for few-shot learning.
Xiantong Zhen*, Yingjun Du*, Haoliang Sun, Jun Xu, Yilong Yin, Ling Shao, Cees Snoek
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Learning to Learn Kernels with Variational Random Features
Learning to Learn with Variational Information Bottleneck for Domain Generalization
ECCV 20
To deal with domain shift, we learn domain-invariant representations by the proposed principle of meta variational information bottleneck, we call MetaVIB.
Yingjun Du, Xiantong Zhen, Jun Xu, Huan Xiong, Qiang Qiu, Ling Shao, Cees Snoek
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Learning to Learn with Variational Information Bottleneck for Domain Generalization

Contact

  • y.du@uva.nl
  • Science Park 900, LAB42, Amsterdam, 1098XH