no code implementations • 9 Feb 2024 • Yichuan Mo, Yuji Wang, Zeming Wei, Yisen Wang
To our knowledge, we are the first to implement defense from the perspective of prompt tuning.
1 code implementation • 14 Oct 2022 • Yichuan Mo, Dongxian Wu, Yifei Wang, Yiwen Guo, Yisen Wang
We find, when randomly masking gradients from some attention blocks or masking perturbations on some patches during adversarial training, the adversarial robustness of ViTs can be remarkably improved, which may potentially open up a line of work to explore the architectural information inside the newly designed models like ViTs.
1 code implementation • 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2022) 2022 • Yichuan Mo, Shilin Wang
In this paper, by observing that deepening the network impairs the performance of the network in detecting unknown attacks, we propose that the synthetic speech detection problem is an out-of-distribution (OOD) generalization problem and we enhance the robustness of networks by using multi-task learning.
no code implementations • 29 Sep 2021 • Yang Li, Yichuan Mo, Liangliang Shi, Junchi Yan, Xiaolu Zhang, Jun Zhou
Although many efforts have been made in terms of backbone architecture design, loss function, and training techniques, few results have been obtained on how the sampling in latent space can affect the final performance, and existing works on latent space mainly focus on controllability.