Search Results for author: Simiao Yu

Found 7 papers, 2 papers with code

Verifying Design through Generative Visualization of Neural Activities

no code implementations28 Mar 2021 Pan Wang, Danlin Peng, Simiao Yu, Chao Wu, Peter Childs, Yike Guo, Ling Li

A recurrent neural network is used as the encoder to learn latent representation from electroencephalogram (EEG) signals, recorded while subjects looked at 50 categories of images.


Topology Distance: A Topology-Based Approach For Evaluating Generative Adversarial Networks

no code implementations27 Feb 2020 Danijela Horak, Simiao Yu, Gholamreza Salimi-Khorshidi

Automatic evaluation of the goodness of Generative Adversarial Networks (GANs) has been a challenge for the field of machine learning.

$¶$ILCRO: Making Importance Landscapes Flat Again

no code implementations27 Jan 2020 Vincent Moens, Simiao Yu, Gholamreza Salimi-Khorshidi

This paper shows that most of the existing convolutional architectures define, at initialisation, a specific feature importance landscape that conditions their capacity to attend to different locations of the images later during training or even at test time.

Feature Importance Image Classification +1

Generative Creativity: Adversarial Learning for Bionic Design

no code implementations19 May 2018 Simiao Yu, Hao Dong, Pan Wang, Chao Wu, Yike Guo

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), resulting in creative biologically-inspired design.

TensorLayer: A Versatile Library for Efficient Deep Learning Development

2 code implementations26 Jul 2017 Hao Dong, Akara Supratak, Luo Mai, Fangde Liu, Axel Oehmichen, Simiao Yu, Yike Guo

Deep learning has enabled major advances in the fields of computer vision, natural language processing, and multimedia among many others.

Semantic Image Synthesis via Adversarial Learning

2 code implementations ICCV 2017 Hao Dong, Simiao Yu, Chao Wu, Yike Guo

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.

Image Generation Image Manipulation

Deep De-Aliasing for Fast Compressive Sensing MRI

no code implementations19 May 2017 Simiao Yu, Hao Dong, Guang Yang, Greg Slabaugh, Pier Luigi Dragotti, Xujiong Ye, Fangde Liu, Simon Arridge, Jennifer Keegan, David Firmin, Yike Guo

Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical applications in order to reduce the scanning cost and improve the patient experience.

Compressive Sensing De-aliasing +1

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