7 citations · 18 across the 5 of their papers we have counts for
5 papers
Tied-Augment: Controlling Representation Similarity Improves Data Augmentation
Emirhan Kurtulus, Zichao Li, Yann Dauphin +1
Data augmentation methods have played an important role in the recent advance of deep learning models, and have become an indispensable component of state-of-the-art models in semi…
On the Adversarial Robustness of Camera-based 3D Object Detection
Shaoyuan Xie, Zichao Li, Zeyu Wang +1
In recent years, camera-based 3D object detection has gained widespread attention for its ability to achieve high performance with low computational cost. However, the robustness o…
Bag of Tricks for FGSM Adversarial Training
Zichao Li, Li Liu, Zeyu Wang +2
Adversarial training (AT) with samples generated by Fast Gradient Sign Method (FGSM), also known as FGSM-AT, is a computationally simple method to train robust networks. However, d…
Overfitting or Underfitting? Understand Robustness Drop in Adversarial Training
Zichao Li, Liyuan Liu, Chengyu Dong +1
Our goal is to understand why the robustness drops after conducting adversarial training for too long. Although this phenomenon is commonly explained as overfitting, our analysis s…
Low-Power Computer Vision: Status, Challenges, Opportunities
Sergei Alyamkin, Matthew Ardi, Alexander C. Berg +41
Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile…