no code implementations • 31 Jan 2024 • Yicui Peng, Hao Chen, ChingSheng Lin, Guo Huang, Jinrong Hu, Hui Guo, Bin Kong, Shu Hu, Xi Wu, Xin Wang
Providing explanations within the recommendation system would boost user satisfaction and foster trust, especially by elaborating on the reasons for selecting recommended items tailored to the user.
1 code implementation • 14 Dec 2021 • Ziwei Luo, Jing Hu, Xin Wang, Siwei Lyu, Bin Kong, Youbing Yin, Qi Song, Xi Wu
Training a model-free deep reinforcement learning model to solve image-to-image translation is difficult since it involves high-dimensional continuous state and action spaces.
1 code implementation • 14 Dec 2021 • Ziwei Luo, Jing Hu, Xin Wang, Shu Hu, Bin Kong, Youbing Yin, Qi Song, Xi Wu, Siwei Lyu
We evaluate our method on several 2D and 3D medical image datasets, some of which contain large deformations.
1 code implementation • 5 Oct 2021 • Peng Liu, Charlie T. Tran, Bin Kong, Ruogu Fang
The proposed training strategy and novel unsupervised domain adaptation framework, called Collaborative Adversarial Domain Adaptation (CADA), can effectively overcome the challenge.
no code implementations • 3 Jun 2021 • Quanyu Liao, Yuezun Li, Xin Wang, Bin Kong, Bin Zhu, Siwei Lyu, Youbing Yin, Qi Song, Xi Wu
Fooling people with highly realistic fake images generated with Deepfake or GANs brings a great social disturbance to our society.
no code implementations • 3 Jun 2021 • Quanyu Liao, Xin Wang, Bin Kong, Siwei Lyu, Bin Zhu, Youbing Yin, Qi Song, Xi Wu
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbation can completely change prediction result.
no code implementations • 27 Oct 2020 • Quanyu Liao, Xin Wang, Bin Kong, Siwei Lyu, Youbing Yin, Qi Song, Xi Wu
The deep neural network is vulnerable to adversarial examples.
no code implementations • 21 Aug 2020 • Dou Xu, Chang Cai, Chaowei Fang, Bin Kong, Jihua Zhu, Zhongyu Li
To thisend, we present a novel method for the unsupervised domain adaptationin histopathological image analysis, based on a backbone for embeddinginput images into a feature space, and a graph neural layer for propa-gating the supervision signals of images with labels.
1 code implementation • Radiology 2020 • Lin Li, Lixin Qin, Zeguo Xu, Youbing Yin, Xin Wang, Bin Kong, Junjie Bai, Yi Lu, Zhenghan Fang, Qi Song, Kunlin Cao, Daliang Liu, Guisheng Wang, Qizhong Xu, Xisheng Fang, Shiqin Zhang, Juan Xia, Jun Xia
Materials and Methods In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19.
no code implementations • 10 Feb 2020 • Quanyu Liao, Xin Wang, Bin Kong, Siwei Lyu, Youbing Yin, Qi Song, Xi Wu
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbations can completely change the classification results.
no code implementations • 5 Feb 2020 • Xian Zhang, Xin Wang, Bin Kong, Youbing Yin, Qi Song, Siwei Lyu, Jiancheng Lv, Canghong Shi, Xiaojie Li
We firstly represent only face regions using the latent variable as the domain knowledge and combine it with the non-face parts textures to generate high-quality face images with plausible contents.
2 code implementations • 16 Oct 2019 • Peng Liu, Bin Kong, Zhongyu Li, Shaoting Zhang, Ruogu Fang
Our proposed CFEA is an interactive paradigm which presents an exquisite of collaborative adaptation through both adversarial learning and ensembling weights.
no code implementations • 30 Sep 2019 • Kia Dashtipour, Mandar Gogate, Jingpeng Li, Fengling Jiang, Bin Kong, Amir Hussain
When no pattern is triggered, the framework switches to its subsymbolic counterpart and leverages deep neural networks (DNN) to perform the classification.
no code implementations • 29 Jan 2019 • Bin Kong, Xin Wang, Junjie Bai, Yi Lu, Feng Gao, Kunlin Cao, Qi Song, Shaoting Zhang, Siwei Lyu, Youbing Yin
In order to address these limitations, we present tree-structured ConvLSTM models for tree-structured image analysis tasks which can be trained end-to-end.
no code implementations • 21 Dec 2018 • Eric Wu, Bin Kong, Xin Wang, Junjie Bai, Yi Lu, Feng Gao, Shaoting Zhang, Kunlin Cao, Qi Song, Siwei Lyu, Youbing Yin
The hierarchical attention components of the residual attention subnet force our network to focus on the key components of the X-ray images and generate the final predictions as well as the associated visual supports, which is similar to the assessment procedure of clinicians.
no code implementations • 25 Aug 2018 • Fengling Jiang, Bin Kong, Ahsan Adeel, Yun Xiao, Amir Hussain
Simultaneously, foreground prior as the virtual absorbing nodes is used to calculate the absorption time and obtain the background possibility.