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Weiwei Liu

5 papers here

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

author position
  • middle author2
  • last author3

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CV1
ORCID 0000-0003-2450-3369
same name
  • Weiwei Liu — 6 papers, h 4
  • Weiwei Liu — 5 papers, h 4
  • Weiwei Liu — 3 papers
  • Weiwei Liu — 2 papers, h 6
  • Weiwei Liu — 2 papers, h 9
  • Weiwei Liu — 2 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

most citedBetter Diffusion Models Further Improve Adversarial Training

53 citations · 61 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2024★ 2 cited

Coverage-Guaranteed Prediction Sets for Out-of-Distribution Data

Xin Zou, Weiwei Liu

Out-of-distribution (OOD) generalization has attracted increasing research attention in recent years, due to its promising experimental results in real-world applications. In this…

cs.LG2023

Deep Partial Multi-Label Learning with Graph Disambiguation

Haobo Wang, Shisong Yang, Gengyu Lyu +5

In partial multi-label learning (PML), each data example is equipped with a candidate label set, which consists of multiple ground-truth labels and other false-positive labels. Rec…

cs.LG2023★ 2 cited

Generalization Bounds for Adversarial Contrastive Learning

Xin Zou, Weiwei Liu

Deep networks are well-known to be fragile to adversarial attacks, and adversarial training is one of the most popular methods used to train a robust model. To take advantage of un…

cs.LG2023★ 4 cited

WAT: Improve the Worst-class Robustness in Adversarial Training

Boqi Li, Weiwei Liu

Deep Neural Networks (DNN) have been shown to be vulnerable to adversarial examples. Adversarial training (AT) is a popular and effective strategy to defend against adversarial att…

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