5 papers · 1 filter
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…
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…
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…
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…
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…