8 citations · 26 across the 14 of their papers we have counts for
14 papers
Elucidating the Design Space of Dataset Condensation
Shitong Shao, Zikai Zhou, Huanran Chen +1
Dataset condensation, a concept within data-centric learning, efficiently transfers critical attributes from an original dataset to a synthetic version, maintaining both diversity…
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…
On the Duality Between Sharpness-Aware Minimization and Adversarial Training
Yihao Zhang, Hangzhou He, Jingyu Zhu +3
Adversarial Training (AT), which adversarially perturb the input samples during training, has been acknowledged as one of the most effective defenses against adversarial attacks, y…
Your Diffusion Model is Secretly a Certifiably Robust Classifier
Huanran Chen, Yinpeng Dong, Shitong Shao +4
Generative learning, recognized for its effective modeling of data distributions, offers inherent advantages in handling out-of-distribution instances, especially for enhancing rob…
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…
AFN: Adaptive Fusion Normalization via an Encoder-Decoder Framework
Zikai Zhou, Shuo Zhang, Ziruo Wang +1
The success of deep learning is inseparable from normalization layers. Researchers have proposed various normalization functions, and each of them has both advantages and disadvant…
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.…
Catch-Up Distillation: You Only Need to Train Once for Accelerating Sampling
Shitong Shao, Xu Dai, Lujun Li +3
Diffusion Probability Models (DPMs) have made impressive advancements in various machine learning domains. However, achieving high-quality synthetic samples typically involves perf…
Rethinking Model Ensemble in Transfer-based Adversarial Attacks
Huanran Chen, Yichi Zhang, Yinpeng Dong +3
It is widely recognized that deep learning models lack robustness to adversarial examples. An intriguing property of adversarial examples is that they can transfer across different…
Teaching What You Should Teach: A Data-Based Distillation Method
Shitong Shao, Huanran Chen, Zhen Huang +3
In real teaching scenarios, an excellent teacher always teaches what he (or she) is good at but the student is not. This gives the student the best assistance in making up for his…
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…
Bootstrap Generalization Ability from Loss Landscape Perspective
Huanran Chen, Shitong Shao, Ziyi Wang +4
Domain generalization aims to learn a model that can generalize well on the unseen test dataset, i.e., out-of-distribution data, which has different distribution from the training…