|
|
|
|
|
Xiaoou Li
This paper tackles the challenge of time series forecasting in the presence of missing data. Traditional methods often struggle with such data, which leads to inaccurate predictions. We propose a novel framework that combines the strengths of Generative ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Lin Yang, Jing Wei, Zejun Zuo and Shunping Zhou
Community roads are crucial to community navigation. There are automatic methods to obtain community roads using trajectories, but the sparsity and uneven density distribution of community trajectories present significant challenges in identifying commun...
ver más
|
|
|
|
|
|
|
|
|
|
|
Jian Xu, Xiaowen Zhou, Chaolin Han, Bing Dong and Hongwei Li
Accurate translation of aerial imagery to maps is a direction of great value and challenge in mapping, a method of generating maps that does not require using vector data as traditional mapping methods do. The tremendous progress made in recent years in ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Huiyuan Wang, Xiaojun Wu, Zirui Wang, Yukun Hao, Chengpeng Hao, Xinyi He and Qiao Hu
Dolphin signals are effective carriers for underwater covert detection and communication. However, the environmental and cost constraints terribly limit the amount of data available in dolphin signal datasets are often limited. Meanwhile, due to the low ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Sampada Tavse, Vijayakumar Varadarajan, Mrinal Bachute, Shilpa Gite and Ketan Kotecha
With the advances in brain imaging, magnetic resonance imaging (MRI) is evolving as a popular radiological tool in clinical diagnosis. Deep learning (DL) methods can detect abnormalities in brain images without an extensive manual feature extraction proc...
ver más
|
|
|
|
|
|
|
|
|
|
|
Fei Ma, Yang Li, Shiguang Ni, Shao-Lun Huang and Lin Zhang
Audio-visual emotion recognition is the research of identifying human emotional states by combining the audio modality and the visual modality simultaneously, which plays an important role in intelligent human-machine interactions. With the help of deep ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Guoqiang Zhou, Yi Fan, Jiachen Shi, Yuyuan Lu and Jun Shen
Generative Adversarial Network (GAN), deemed as a powerful deep-learning-based silver bullet for intelligent data generation, has been widely used in multi-disciplines. Furthermore, conditional GAN (CGAN) introduces artificial control information on the ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Wang Xi, Guillaume Devineau, Fabien Moutarde and Jie Yang
Generative models for images, audio, text, and other low-dimension data have achieved great success in recent years. Generating artificial human movements can also be useful for many applications, including improvement of data augmentation methods for hu...
ver más
|
|
|
|
|
|
|
|
|
|
|
Shayan Taheri, Aminollah Khormali, Milad Salem and Jiann-Shiun Yuan
In this work, we propose a novel defense system against adversarial examples leveraging the unique power of Generative Adversarial Networks (GANs) to generate new adversarial examples for model retraining. To do so, we develop an automated pipeline using...
ver más
|
|
|
|
|
|
|
|
|
|
|
Joel R. Bock and Akhilesh Maewal
Product recommendation can be considered as a problem in data fusion?estimation of the joint distribution between individuals, their behaviors, and goods or services of interest. This work proposes a conditional, coupled generative adversarial network (R...
ver más
|
|
|
|
|
|