1.4k citations · 1.8k across the 7 of their papers we have counts for
7 papers
Verifying Design through Generative Visualization of Neural Activities
Pan Wang, Danlin Peng, Simiao Yu +4
Current neuroscience focused approaches for evaluating the effectiveness of a design do not use direct visualisation of mental activity. A recurrent neural network is used as the e…
Generative Creativity: Adversarial Learning for Bionic Design
Simiao Yu, Hao Dong, Pan Wang +2
Bionic design refers to an approach of generative creativity in which a target object (e.g. a floor lamp) is designed to contain features of biological source objects (e.g. flowers…
Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security
Hao Dong, Chao Wu, Zhen Wei +1
Deep learning methods can play a crucial role in anomaly detection, prediction, and supporting decision making for applications like personal health-care, pervasive body sensing, e…
Semantic Image Synthesis via Adversarial Learning
Hao Dong, Simiao Yu, Chao Wu +1
In this paper, we propose a way of synthesizing realistic images directly with natural language description, which has many useful applications, e.g. intelligent image manipulation…
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
Akara Supratak, Hao Dong, Chao Wu +1
The present study proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-e…
Unsupervised Image-to-Image Translation with Generative Adversarial Networks
Hao Dong, Paarth Neekhara, Chao Wu +1
It's useful to automatically transform an image from its original form to some synthetic form (style, partial contents, etc.), while keeping the original structure or semantics. We…