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Zhen Liu

Megvii Technology

14 papers hereh-index 13879 citations24 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author10
  • last author1

Across the 13 of 14 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • cs.CV4
  • eess.IV2
  • cs.AI1
affiliations
  • Megvii Technology
ORCID 0000-0002-2804-1133
same name
  • Zhen Liu — 57 papers, h 50
  • Zhen Liu — 34 papers, h 26
  • Zhen Liu — 15 papers
  • Zhen Liu — 13 papers, h 7
  • Zhen Liu — 12 papers, h 4
  • Zhen Liu — 11 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172023
most citedDeep Hyperspherical Learning

56 citations · 119 across the 14 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.LG2021★ 1 cited

Iterative Teaching by Label Synthesis

Weiyang Liu, Zhen Liu, Hanchen Wang +3

In this paper, we consider the problem of iterative machine teaching, where a teacher provides examples sequentially based on the current iterative learner. In contrast to previous…

cs.AI2021★ 10 cited

A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning

Mingde Zhao, Zhen Liu, Sitao Luan +3

We present an end-to-end, model-based deep reinforcement learning agent which dynamically attends to relevant parts of its state during planning. The agent uses a bottleneck mechan…

cs.CV2021★ 7 cited

ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging

Zhen Liu, Wenjie Lin, Xinpeng Li +6

In this paper, we present an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet. This problem comprises two…

cs.LG2021

Learning with Hyperspherical Uniformity

Weiyang Liu, Rongmei Lin, Zhen Liu +3

Due to the over-parameterization nature, neural networks are a powerful tool for nonlinear function approximation. In order to achieve good generalization on unseen data, a suitabl…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.