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pro vyhledávání: '"Roh, Jaechul"'
Text-conditional diffusion models, i.e. text-to-image, produce eye-catching images that represent descriptions given by a user. These images often depict benign concepts but could also carry other purposes. Specifically, visual information is easy to
Externí odkaz:
http://arxiv.org/abs/2406.15213
We introduce One-Shot Label-Only (OSLO) membership inference attacks (MIAs), which accurately infer a given sample's membership in a target model's training set with high precision using just \emph{a single query}, where the target model only returns
Externí odkaz:
http://arxiv.org/abs/2405.16978
Multimodal machine learning, especially text-to-image models like Stable Diffusion and DALL-E 3, has gained significance for transforming text into detailed images. Despite their growing use and remarkable generative capabilities, there is a pressing
Externí odkaz:
http://arxiv.org/abs/2312.07550
Diffusion-based models, such as the Stable Diffusion model, have revolutionized text-to-image synthesis with their ability to produce high-quality, high-resolution images. These advancements have prompted significant progress in image generation and
Externí odkaz:
http://arxiv.org/abs/2312.03692
Autor:
Roh, Jaechul, Fang, Yajun
Over the past few years, the field of adversarial attack received numerous attention from various researchers with the help of successful attack success rate against well-known deep neural networks that were acknowledged to achieve high classificatio
Externí odkaz:
http://arxiv.org/abs/2211.05410
Pre-trained language models allowed us to process downstream tasks with the help of fine-tuning, which aids the model to achieve fairly high accuracy in various Natural Language Processing (NLP) tasks. Such easily-downloaded language models from vari
Externí odkaz:
http://arxiv.org/abs/2211.05371
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