1 code implementation • • Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen, Pengtao Xie
We also study the transferability of models trained on MedDialog to low-resource medical dialogue generation tasks.
This work presents a novel First-person View based Trajectory predicting model (FvTraj) to estimate the future trajectories of pedestrians in a scene given their observed trajectories and the corresponding first-person view images.
Our attacks faithfully reconstruct private texts included in training data with access to the target model.
We introduce a training strategy called ``Differentiable Architecture Search with a Generative Model(DASGM)."
Based on these two ideas, we propose a TreeGAN model which consists of three modules: (1) a class hierarchy encoder (CHE) which takes the hierarchical structure of classes and their textual names as inputs and learns an embedding for each class; the embedding captures the hierarchical relationship among classes; (2) a conditional image generator (CIG) which takes the CHE-generated embedding of a class as input and generates a set of images belonging to this class; (3) a consistency checker which performs hierarchical classification on the generated images and checks whether the generated images are compatible with the class hierarchy; the consistency score is used to guide the CIG to generate hierarchy-compatible images.
Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs.