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Precise Knowledge Transfer via Flow Matching
Shitong Shao, Zhiqiang Shen, Linrui Gong +2
In this paper, we propose a novel knowledge transfer framework that introduces continuous normalizing flows for progressive knowledge transformation and leverages multi-step sampli…
Rethinking Centered Kernel Alignment in Knowledge Distillation
Zikai Zhou, Yunhang Shen, Shitong Shao +2
Knowledge distillation has emerged as a highly effective method for bridging the representation discrepancy between large-scale models and lightweight models. Prevalent approaches…
How Robust is Google's Bard to Adversarial Image Attacks?
Yinpeng Dong, Huanran Chen, Jiawei Chen +6
Multimodal Large Language Models (MLLMs) that integrate text and other modalities (especially vision) have achieved unprecedented performance in various multimodal tasks. However,…
Enhancing Adversarial Attacks: The Similar Target Method
Shuo Zhang, Ziruo Wang, Zikai Zhou +1
Deep neural networks are vulnerable to adversarial examples, posing a threat to the models' applications and raising security concerns. An intriguing property of adversarial exampl…
Robust Classification via a Single Diffusion Model
Huanran Chen, Yinpeng Dong, Zhengyi Wang +4
Diffusion models have been applied to improve adversarial robustness of image classifiers by purifying the adversarial noises or generating realistic data for adversarial training.…
T-SEA: Transfer-based Self-Ensemble Attack on Object Detection
Hao Huang, Ziyan Chen, Huanran Chen +2
Compared to query-based black-box attacks, transfer-based black-box attacks do not require any information of the attacked models, which ensures their secrecy. However, most existi…