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researcher

Di Wang

4 papers hereh-index 5252 citations6 works total

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

author position
  • middle author2
  • last author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV2
  • cs.CR1
  • cs.LG1
same name
  • Di Wang — 18 papers, h 8
  • Di Wang — 16 papers, h 15
  • Di Wang — 15 papers, h 10
  • Di Wang — 14 papers, h 4
  • Di Wang — 14 papers, h 6
  • Di Wang — 12 papers, h 5

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 citedAn LLM can Fool Itself: A Prompt-Based Adversarial Attack

13 citations · 13 across the 1 of their papers we have counts for

collaborators

4 papers

cs.CV2024

Text Guided Image Editing with Automatic Concept Locating and Forgetting

Jia Li, Lijie Hu, Zhixian He +3

With the advancement of image-to-image diffusion models guided by text, significant progress has been made in image editing. However, a persistent challenge remains in seamlessly i…

cs.LG2024

Privacy-Preserving Low-Rank Adaptation against Membership Inference Attacks for Latent Diffusion Models

Zihao Luo, Xilie Xu, Feng Liu +3

Low-rank adaptation (LoRA) is an efficient strategy for adapting latent diffusion models (LDMs) on a private dataset to generate specific images by minimizing the adaptation loss.…

cs.CV2023

Fair Text-to-Image Diffusion via Fair Mapping

Jia Li, Lijie Hu, Jingfeng Zhang +3

In this paper, we address the limitations of existing text-to-image diffusion models in generating demographically fair results when given human-related descriptions. These models…

cs.CR2023★ 13 cited

An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Xilie Xu, Keyi Kong, Ning Liu +4

The wide-ranging applications of large language models (LLMs), especially in safety-critical domains, necessitate the proper evaluation of the LLM's adversarial robustness. This pa…

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