7 citations · 36 across the 20 of their papers we have counts for
8 papers
A Theoretical Explanation of Activation Sparsity through Flat Minima and Adversarial Robustness
Ze Peng, Lei Qi, Yinghuan Shi +1
A recent empirical observation (Li et al., 2022b) of activation sparsity in MLP blocks offers an opportunity to drastically reduce computation costs for free. Although having attri…
FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models
Lihe Yang, Xiaogang Xu, Bingyi Kang +2
Semantic segmentation has witnessed tremendous progress due to the proposal of various advanced network architectures. However, they are extremely hungry for delicate annotations t…
Exploring Flat Minima for Domain Generalization with Large Learning Rates
Jian Zhang, Lei Qi, Yinghuan Shi +1
Domain Generalization (DG) aims to generalize to arbitrary unseen domains. A promising approach to improve model generalization in DG is the identification of flat minima. One typi…
Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learning
Guan Gui, Zhen Zhao, Lei Qi +3
In semi-supervised learning, unlabeled samples can be utilized through augmentation and consistency regularization. However, we observed certain samples, even undergoing strong aug…
ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization
Jintao Guo, Na Wang, Lei Qi +1
Domain generalization (DG) aims to learn a model that generalizes well to unseen target domains utilizing multiple source domains without re-training. Most existing DG works are ba…
Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation
Heng Cai, Shumeng Li, Lei Qi +3
Recent trends in semi-supervised learning have significantly boosted the performance of 3D semi-supervised medical image segmentation. Compared with 2D images, 3D medical volumes i…