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Kun He

Huazhong University of Science and Technology

17 papers hereh-index 222.9k citations80 works total

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

author position
  • first author5
  • middle author5
  • last author6

Across the 16 of 17 papers where every author was matched, so the position is known.

fields
  • cs.LG5
  • cs.CV4
  • cs.AI3
  • cs.SI2
  • cs.DS1
  • cs.IR1
affiliations
  • Huazhong University of Science and Technology
Homepage
same name
  • Kun He — 18 papers
  • Kun He — 14 papers
  • Kun He — 11 papers
  • Kun He — 11 papers, h 10
  • Kun He — 6 papers
  • Kun He — 5 papers, h 6

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
20162022
most citedBoosting Adversarial Transferability through Enhanced Momentum

27 citations · 40 across the 13 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2022

Class-aware Information for Logit-based Knowledge Distillation

Shuoxi Zhang, Hanpeng Liu, John E. Hopcroft +1

Knowledge distillation aims to transfer knowledge to the student model by utilizing the predictions/features of the teacher model, and feature-based distillation has recently shown…

cs.CV2021★ 27 cited

Boosting Adversarial Transferability through Enhanced Momentum

Xiaosen Wang, Jiadong Lin, Han Hu +2

Deep learning models are known to be vulnerable to adversarial examples crafted by adding human-imperceptible perturbations on benign images. Many existing adversarial attack metho…

cs.CV2017★ 2 cited

The Local Dimension of Deep Manifold

Mengxiao Zhang, Wangquan Wu, Yanren Zhang +4

Based on our observation that there exists a dramatic drop for the singular values of the fully connected layers or a single feature map of the convolutional layer, and that the di…

cs.CV2016

A Powerful Generative Model Using Random Weights for the Deep Image Representation

Kun He, Yan Wang, John Hopcroft

To what extent is the success of deep visualization due to the training? Could we do deep visualization using untrained, random weight networks? To address this issue, we explore n…

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