activity
20172024
most citedTowards Interpretable Deep Neural Networks by Leveraging Adversarial Examples

88 citations · 135 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV20241 cited

Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models

Fan Bao, Chendong Xiang, Gang Yue +7

We introduce Vidu, a high-performance text-to-video generator that is capable of producing 1080p videos up to 16 seconds in a single generation. Vidu is a diffusion model with U-Vi…

cs.LG20225 cited

Why Are Conditional Generative Models Better Than Unconditional Ones?

Fan Bao, Chongxuan Li, Jiacheng Sun +1

Extensive empirical evidence demonstrates that conditional generative models are easier to train and perform better than unconditional ones by exploiting the labels of data. So do…

cs.LG20212 cited

Stability and Generalization of Bilevel Programming in Hyperparameter Optimization

Fan Bao, Guoqiang Wu, Chongxuan Li +2

The (gradient-based) bilevel programming framework is widely used in hyperparameter optimization and has achieved excellent performance empirically. Previous theoretical work mainl…

cs.LG2020

Bi-level Score Matching for Learning Energy-based Latent Variable Models

Fan Bao, Chongxuan Li, Kun Xu +3

Score matching (SM) provides a compelling approach to learn energy-based models (EBMs) by avoiding the calculation of partition function. However, it remains largely open to learn…

cs.LG2019

Boosting Generative Models by Leveraging Cascaded Meta-Models

Fan Bao, Hang Su, Jun Zhu

Deep generative models are effective methods of modeling data. However, it is not easy for a single generative model to faithfully capture the distributions of complex data such as…

cs.LG201988 cited

Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples

Yinpeng Dong, Fan Bao, Hang Su +1

Sometimes it is not enough for a DNN to produce an outcome. For example, in applications such as healthcare, users need to understand the rationale of the decisions. Therefore, it…