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Zihao Wang

7 papers hereh-index 9826 citations22 works total

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

author position
  • first author4
  • middle author3

Across the 7 of 7 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.LG3
same name
  • Zihao Wang — 25 papers, h 16
  • Zihao Wang — 19 papers, h 15
  • Zihao Wang — 17 papers, h 7
  • Zihao Wang — 14 papers, h 19
  • Zihao Wang — 11 papers, h 5
  • Zihao Wang — 11 papers, h 3

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
20192022
most citedCLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP

31 citations · 62 across the 6 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2023

Score-based Generative Modeling Through Backward Stochastic Differential Equations: Inversion and Generation

Zihao Wang

The proposed BSDE-based diffusion model represents a novel approach to diffusion modeling, which extends the application of stochastic differential equations (SDEs) in machine lear…

cs.LG2022

Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration

Manyi Zhang, Yuxin Ren, Zihao Wang +1

Instance-dependent label noise is realistic but rather challenging, where the label-corruption process depends on instances directly. It causes a severe distribution shift between…

cs.LG2020

End-To-End Graph-based Deep Semi-Supervised Learning

Zihao Wang, Enmei Tu, Zhou Meng

The quality of a graph is determined jointly by three key factors of the graph: nodes, edges and similarity measure (or edge weights), and is very crucial to the success of graph-b…

cs.LG2019

Semi-Supervised Deep Learning Using Improved Unsupervised Discriminant Projection

Xiao Han, Zihao Wang, Enmei Tu +2

Deep learning demands a huge amount of well-labeled data to train the network parameters. How to use the least amount of labeled data to obtain the desired classification accuracy…

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