no code implementations • 9 Oct 2023 • Yong Lin, Fan Zhou, Lu Tan, Lintao Ma, Jiameng Liu, Yansu He, Yuan Yuan, Yu Liu, James Zhang, Yujiu Yang, Hao Wang
To address this challenge, we then propose Continuous Invariance Learning (CIL), which extracts invariant features across continuously indexed domains.
no code implementations • 29 Sep 2023 • Yong Lin, Lu Tan, Yifan Hao, Honam Wong, Hanze Dong, Weizhong Zhang, Yujiu Yang, Tong Zhang
Contrary to the conventional wisdom that focuses on learning invariant features for better OOD performance, our findings suggest that incorporating a large number of diverse spurious features weakens their individual contributions, leading to improved overall OOD generalization performance.
1 code implementation • 11 Mar 2022 • Yong Lin, Shengyu Zhu, Lu Tan, Peng Cui
When data are divided into distinct environments according to the heterogeneity, recent invariant learning methods have proposed to learn robust and invariant models based on this environment partition.
no code implementations • 28 Mar 2020 • Huizhu Pan, Jintao Song, Wanquan Liu, Ling Li, Guanglu Zhou, Lu Tan, Shichu Chen
Preserving contour topology during image segmentation is useful in many practical scenarios.
no code implementations • 20 Feb 2019 • Lu Tan, Ling Li, Wanquan Liu, Jie Sun, Min Zhang
Euler's Elastica based unsupervised segmentation models have strong capability of completing the missing boundaries for existing objects in a clean image, but they are not working well for noisy images.