Aesthetic Style Transfer through Text-to-image Synthesis and Image-to-image Translation

2019 ◽  
Author(s):  
Megumi Kotera ◽  
Ren Togo ◽  
Takahiro Ogawa ◽  
Miki Haseyama
Keyword(s):  
Image Synthesis ◽  
Style Transfer ◽  
Image Translation ◽  
Aesthetic Style

scholarly journals Unpaired Image- to- Image Translation using Cycle Generative Adversarial Networks

2020 ◽  
Vol 9 (6) ◽  
pp. 380-385
Keyword(s):  
Mapping Function ◽  
Image Synthesis ◽  
Input Image ◽  
General Purpose ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Inverse Mapping ◽  
Style Transfer ◽  
Adversarial Networks ◽  
Image Translation

In this burgeoning age and society where people are tending towards learning the benefits adversarial network we hereby benefiting the society tend to extend our research towards adversarial networks as a general-purpose solution to image-to-image translation problems. Image to image translation comes under the peripheral class of computer sciences extending our branch in the field of neural networks. We apprentice Generative adversarial networks as an optimum solution for generating Image to image translation where our motive is to learn a mapping between an input image(X) and an output image(Y) using a set of predefined pairs[4]. But it is not necessary that the paired dataset is provided to for our use and hence adversarial methods comes into existence. Further, we advance a method that is able to convert and recapture an image from a domain X to another domain Y in the absence of paired datasets. Our objective is to learn a mapping function G: A —B such that the mapping is able to distinguish the images of G(A) within the distribution of B using an adversarial loss.[1] Because this mapping is high biased, we introduce an inverse mapping function F B—A and introduce a cycle consistency loss[7]. Furthermore we wish to extend our research with various domains and involve them with neural style transfer, semantic image synthesis. Our essential commitment is to show that on a wide assortment of issues, conditional GANs produce sensible outcomes. This paper hence calls for the attention to the purpose of converting image X to image Y and we commit to the transfer learning of training dataset and optimising our code.You can find the source code for the same here.

scholarly journals Medical image processing with contextual style transfer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yin Xu ◽  
Yan Li ◽  
Byeong-Seok Shin
Keyword(s):  
Medical Image ◽  
Medical Image Processing ◽  
Image Synthesis ◽  
Industrial Applications ◽  
Generative Models ◽  
Context Aware ◽  
Training Set ◽  
Style Transfer ◽  
Segmentation Accuracy ◽  
Generative Methods

Abstract With recent advances in deep learning research, generative models have achieved great achievements and play an increasingly important role in current industrial applications. At the same time, technologies derived from generative methods are also under a wide discussion with researches, such as style transfer, image synthesis and so on. In this work, we treat generative methods as a possible solution to medical image augmentation. We proposed a context-aware generative framework, which can successfully change the gray scale of CT scans but almost without any semantic loss. By producing target images that with specific style / distribution, we greatly increased the robustness of segmentation model after adding generations into training set. Besides, we improved 2– 4% pixel segmentation accuracy over original U-NET in terms of spine segmentation. Lastly, we compared generations produced by networks when using different feature extractors (Vgg, ResNet and DenseNet) and made a detailed analysis on their performances over style transfer.

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.

scholarly journals Study on Optimal Generative Network for Synthesizing Brain Tumor-Segmented MR Images

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hyunhee Lee ◽  
Jaechoon Jo ◽  
Heuiseok Lim
Keyword(s):  
Brain Tumor ◽  
Medical Imaging ◽  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
Imaging Data ◽  
Mr Images ◽  
Style Transfer ◽  
Adversarial Networks ◽  
Robust Model ◽  
Privacy Issues

Due to institutional and privacy issues, medical imaging researches are confronted with serious data scarcity. Image synthesis using generative adversarial networks provides a generic solution to the lack of medical imaging data. We synthesize high-quality brain tumor-segmented MR images, which consists of two tasks: synthesis and segmentation. We performed experiments with two different generative networks, the first using the ResNet model, which has significant advantages of style transfer, and the second, the U-Net model, one of the most powerful models for segmentation. We compare the performance of each model and propose a more robust model for synthesizing brain tumor-segmented MR images. Although ResNet produced better-quality images than did U-Net for the same samples, it used a great deal of memory and took much longer to train. U-Net, meanwhile, segmented the brain tumors more accurately than did ResNet.

Handwritten Signature Spoofing With Conditional Generative Adversarial Nets

2022 ◽  
pp. 98-110
Author(s):  
Md Fazle Rabby ◽  
Md Abdullah Al Momin ◽  
Xiali Hei
Keyword(s):  
Computer Vision ◽  
Image Synthesis ◽  
Research Topic ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Image Translation ◽  
Handwritten Signature ◽  
Condition Vector

Generative adversarial networks have been a highly focused research topic in computer vision, especially in image synthesis and image-to-image translation. There are a lot of variations in generative nets, and different GANs are suitable for different applications. In this chapter, the authors investigated conditional generative adversarial networks to generate fake images, such as handwritten signatures. The authors demonstrated an implementation of conditional generative adversarial networks, which can generate fake handwritten signatures according to a condition vector tailored by humans.

scholarly journals CBNWI-50: A Deep Learning Bird Dataset for Image Translation and Resolution Improvement using Generative Adversarial Network

2019 ◽  
Vol 8 (9S) ◽  
pp. 91-102
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Western India ◽  
Generative Adversarial Network ◽  
Style Transfer ◽  
Adversarial Network ◽  
Image Translation ◽  
Common Birds ◽  
Single Image Super Resolution

Generative Adversarial Networks have gained prominence in a short span of time as they can synthesize images from latent noise by minimizing the adversarial cost function. New variants of GANs have been developed to perform specific tasks using state-of-the-art GAN models, like image translation, single image super resolution, segmentation, classification, style transfer etc. However, a combination of two GANs to perform two different applications in one model has been sparsely explored. Hence, this paper concatenates two GANs and aims to perform Image Translation using Cycle GAN model on bird images and improve their resolution using SRGAN. During the extensive survey, it is observed that most of the deep learning databases on Aves were built using the new world species (i.e. species found in North America). Hence, to bridge this gap, a new Ave database, 'Common Birds of North - Western India' (CBNWI-50), is also proposed in this work.

scholarly journals MapGAN: An Intelligent Generation Model for Network Tile Maps

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3119 ◽  
Author(s):  
Jingtao Li ◽  
Zhanlong Chen ◽  
Xiaozhen Zhao ◽  
Lijia Shao
Keyword(s):  
Image Inpainting ◽  
Super Resolution ◽  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
Great Success ◽  
Generation Model ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Image Translation ◽  
Map Generation

In recent years, the generative adversarial network (GAN)-based image translation model has achieved great success in image synthesis, image inpainting, image super-resolution, and other tasks. However, the images generated by these models often have problems such as insufficient details and low quality. Especially for the task of map generation, the generated electronic map cannot achieve effects comparable to industrial production in terms of accuracy and aesthetics. This paper proposes a model called Map Generative Adversarial Networks (MapGAN) for generating multitype electronic maps accurately and quickly based on both remote sensing images and render matrices. MapGAN improves the generator architecture of Pix2pixHD and adds a classifier to enhance the model, enabling it to learn the characteristics and style differences of different types of maps. Using the datasets of Google Maps, Baidu maps, and Map World maps, we compare MapGAN with some recent image translation models in the fields of one-to-one map generation and one-to-many domain map generation. The results show that the quality of the electronic maps generated by MapGAN is optimal in terms of both intuitive vision and classic evaluation indicators.

scholarly journals Text to Image Translation using Cycle GAN

2020 ◽  
Vol 9 (4) ◽  
pp. 1294-1297
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
Image Synthesis ◽  
Difficult Problem ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Current State ◽  
Image Translation ◽  
Translation Methods ◽  
Text Images

In the recent past, text-to-image translation was an active field of research. The ability of a network to know a sentence's context and to create a specific picture that represents the sentence demonstrates the model's ability to think more like humans. Common text--translation methods employ Generative Adversarial Networks to generate high-text-images, but the images produced do not always represent the meaning of the phrase provided to the model as input. Using a captioning network to caption generated images, we tackle this problem and exploit the gap between ground truth captions and generated captions to further enhance the network. We present detailed similarities between our system and the methods already in place. Text-to-Image synthesis is a difficult problem with plenty of space for progress despite the current state-of - the-art results. Synthesized images from current methods give the described image a rough sketch but do not capture the true essence of what the text describes. The re-penny achievement of Generative Adversarial Networks (GANs) demonstrates that they are a decent contender for the decision of design to move toward this issue.

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