An unsupervised font style transfer model based on generative adversarial networks

2021 ◽  
Author(s):  
Sihan Zeng ◽  
Zhongliang Pan
Keyword(s):  
Generative Adversarial Networks ◽  
Transfer Model ◽  
Style Transfer ◽  
Model Based ◽  
Adversarial Networks

A model-based reinforcement learning method based on conditional generative adversarial networks

2021 ◽  
Vol 152 ◽  
pp. 18-25
Author(s):  
Tingting Zhao ◽  
Ying Wang ◽  
Guixi Li ◽  
Le Kong ◽  
Yarui Chen ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Generative Adversarial Networks ◽  
Learning Method ◽  
Model Based ◽  
Adversarial Networks

A Traffic Video Completion Model Based on Generative Adversarial Networks

2021 ◽  
pp. 658-669
Author(s):  
Lan Wu ◽  
Han Wang ◽  
Tian Gao ◽  
Binquan Li ◽  
Fanshi Kong
Keyword(s):  
Generative Adversarial Networks ◽  
Model Based ◽  
Adversarial Networks ◽  
Video Completion ◽  
Traffic Video

scholarly journals Parkinson’s Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2605 ◽  
Author(s):  
Rafael Anicet Zanini ◽  
Esther Luna Colombini
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Data Augmentation ◽  
Experimental Results ◽  
Generative Adversarial Networks ◽  
Style Transfer ◽  
Treatment Approaches ◽  
Adversarial Networks

This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson’s Disease (PD) electromyography (EMG) signals. The experimental results indicate that the proposed models can adapt to different frequencies and amplitudes of tremor, simulating each patient’s tremor patterns and extending them to different sets of movement protocols. Therefore, one could use these models for extending the existing patient dataset and generating tremor simulations for validating treatment approaches on different movement scenarios.

scholarly journals Research on image style transfer method based on Generative Adversarial Networks

2019 ◽  
Vol 1314 ◽  
pp. 012191
Author(s):  
Jie Wu ◽  
Jin Duan ◽  
Jinqiang Yu ◽  
Haodong Shi ◽  
Yingchao Li
Keyword(s):  
Generative Adversarial Networks ◽  
Style Transfer ◽  
Adversarial Networks ◽  
Transfer Method

scholarly journals Play as You Like: Timbre-Enhanced Multi-Modal Music Style Transfer

2019 ◽  
Vol 33 ◽  
pp. 1061-1068 ◽  
Author(s):  
Chien-Yu Lu ◽  
Min-Xin Xue ◽  
Chia-Che Chang ◽  
Che-Rung Lee ◽  
Li Su
Keyword(s):  
String Quartet ◽  
Generative Adversarial Networks ◽  
Spectral Difference ◽  
Mel Frequency Cepstral Coefficients ◽  
Style Transfer ◽  
Piano Solo ◽  
Adversarial Networks ◽  
Channel Input ◽  
Image Translation ◽  
Transfer Method

Style transfer of polyphonic music recordings is a challenging task when considering the modeling of diverse, imaginative, and reasonable music pieces in the style different from their original one. To achieve this, learning stable multi-modal representations for both domain-variant (i.e., style) and domaininvariant (i.e., content) information of music in an unsupervised manner is critical. In this paper, we propose an unsupervised music style transfer method without the need for parallel data. Besides, to characterize the multi-modal distribution of music pieces, we employ the Multi-modal Unsupervised Image-to-Image Translation (MUNIT) framework in the proposed system. This allows one to generate diverse outputs from the learned latent distributions representing contents and styles. Moreover, to better capture the granularity of sound, such as the perceptual dimensions of timbre and the nuance in instrument-specific performance, cognitively plausible features including mel-frequency cepstral coefficients (MFCC), spectral difference, and spectral envelope, are combined with the widely-used mel-spectrogram into a timbreenhanced multi-channel input representation. The Relativistic average Generative Adversarial Networks (RaGAN) is also utilized to achieve fast convergence and high stability. We conduct experiments on bilateral style transfer tasks among three different genres, namely piano solo, guitar solo, and string quartet. Results demonstrate the advantages of the proposed method in music style transfer with improved sound quality and in allowing users to manipulate the output.

Style Transfer of Urban Road Images Using Generative Adversarial Networks With Structural Details

2020 ◽  
Vol 27 (3) ◽  
pp. 54-65
Author(s):  
Yaochen Li ◽  
Xiao Wu ◽  
Danhui Lu ◽  
Ling Li ◽  
Yuehu Liu ◽  
...  
Keyword(s):  
Generative Adversarial Networks ◽  
Style Transfer ◽  
Urban Road ◽  
Adversarial Networks ◽  
Structural Details
Sign in / Sign up

Export Citation Format

Share Document