1 code implementation • NeurIPS 2021 • Xinwei Sun, Botong Wu, Xiangyu Zheng, Chang Liu, Wei Chen, Tao Qin, Tie-Yan Liu
To avoid such a spurious correlation, we propose \textbf{La}tent \textbf{C}ausal \textbf{I}nvariance \textbf{M}odels (LaCIM) that specifies the underlying causal structure of the data and the source of distributional shifts, guiding us to pursue only causal factor for prediction.
no code implementations • CVPR 2021 • Jing Li, Botong Wu, Xinwei Sun, Yizhou Wang
We propose a causal hidden Markov model to achieve robust prediction of irreversible disease at an early stage, which is safety-critical and vital for medical treatment in early stages.
no code implementations • CVPR 2021 • Botong Wu, Sijie Ren, Jing Li, Xinwei Sun, Shiming Li, Yizhou Wang
In order to account for the degree of progression of the disease, we propose a temporal generative model to accurately generate the future image and compare it with the current one to get a residual image.
no code implementations • 19 Dec 2020 • Xinwei Sun, Botong Wu, Wei Chen
To learn such an invariance for deepfake detection, our InTeLe introduces an auto-encoder framework with different decoders for pristine and fake images, which are further appended with a shallow classifier in order to separate out the obvious artifact-effect.
no code implementations • 4 Nov 2020 • Xinwei Sun, Botong Wu, Xiangyu Zheng, Chang Liu, Wei Chen, Tao Qin, Tie-Yan Liu
To avoid spurious correlation, we propose a Latent Causal Invariance Model (LaCIM) which pursues causal prediction.
no code implementations • ICCV 2019 • Botong Wu, Xinwei Sun, Lingjing Hu, Yizhou Wang
The benefits of learning with unsure data and validity of our models are demonstrated on the prediction of Alzheimer's Disease and lung nodules.
no code implementations • 10 Feb 2018 • Botong Wu, Zhen Zhou, Jianwei Wang, Yizhou Wang
Refer to the literature of lung nodule classification, many studies adopt Convolutional Neural Networks (CNN) to directly predict the malignancy of lung nodules with original thoracic Computed Tomography (CT) and nodule location.
no code implementations • 2 Dec 2016 • Bo Zhao, Botong Wu, Tianfu Wu, Yizhou Wang
This paper presents a method of zero-shot learning (ZSL) which poses ZSL as the missing data problem, rather than the missing label problem.