scholarly journals A Study on Persistence of GAN-Based Vision-Induced Gustatory Manipulation

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1157
Author(s):  
Kizashi Nakano ◽  
Daichi Horita ◽  
Norihiko Kawai ◽  
Naoya Isoyama ◽  
Nobuchika Sakata ◽  
...  
Keyword(s):  
User Study ◽  
Small Population ◽  
Translation System ◽  
Cultural Backgrounds ◽  
Generative Adversarial Network ◽  
Head Mounted Display ◽  
Adversarial Network ◽  
Image Translation ◽  
Visual Modulation ◽  
Changes Over Time

Vision-induced gustatory manipulation interfaces can help people with dietary restrictions feel as if they are eating what they want by modulating the appearance of the alternative foods they are eating in reality. However, it is still unclear whether vision-induced gustatory change persists beyond a single bite, how the sensation changes over time, and how it varies among individuals from different cultural backgrounds. The present paper reports on a user study conducted to answer these questions using a generative adversarial network (GAN)-based real-time image-to-image translation system. In the user study, 16 participants were presented somen noodles or steamed rice through a video see-through head mounted display (HMD) both in two conditions; without or with visual modulation (somen noodles and steamed rice were translated into ramen noodles and curry and rice, respectively), and brought food to the mouth and tasted it five times with an interval of two minutes. The results of the experiments revealed that vision-induced gustatory manipulation is persistent in many participants. Their persistent gustatory changes are divided into three groups: those in which the intensity of the gustatory change gradually increased, those in which it gradually decreased, and those in which it did not fluctuate, each with about the same number of participants. Although the generalizability is limited due to the small population, it was also found that non-Japanese and male participants tended to perceive stronger gustatory manipulation compared to Japanese and female participants. We believe that our study deepens our understanding and insight into vision-induced gustatory manipulation and encourages further investigation.

scholarly journals Multimodal Structure-Consistent Image-to-Image Translation

2020 ◽  
Vol 34 (07) ◽  
pp. 11490-11498
Author(s):  
Che-Tsung Lin ◽  
Yen-Yi Wu ◽  
Po-Hao Hsu ◽  
Shang-Hong Lai
Keyword(s):  
Data Augmentation ◽  
Network Models ◽  
Detection Accuracy ◽  
Target Domain ◽  
Generative Adversarial Network ◽  
Image Objects ◽  
Perceptual Distance ◽  
Adversarial Network ◽  
Image Translation ◽  
Realistic Images

Unpaired image-to-image translation is proven quite effective in boosting a CNN-based object detector for a different domain by means of data augmentation that can well preserve the image-objects in the translated images. Recently, multimodal GAN (Generative Adversarial Network) models have been proposed and were expected to further boost the detector accuracy by generating a diverse collection of images in the target domain, given only a single/labelled image in the source domain. However, images generated by multimodal GANs would achieve even worse detection accuracy than the ones by a unimodal GAN with better object preservation. In this work, we introduce cycle-structure consistency for generating diverse and structure-preserved translated images across complex domains, such as between day and night, for object detector training. Qualitative results show that our model, Multimodal AugGAN, can generate diverse and realistic images for the target domain. For quantitative comparisons, we evaluate other competing methods and ours by using the generated images to train YOLO, Faster R-CNN and FCN models and prove that our model achieves significant improvement and outperforms other methods on the detection accuracies and the FCN scores. Also, we demonstrate that our model could provide more diverse object appearances in the target domain through comparison on the perceptual distance metric.

scholarly journals Accurate and Consistent Image-to-Image Conditional Adversarial Network

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 395 ◽  
Author(s):  
Naeem Ul Islam ◽  
Sungmin Lee ◽  
Jaebyung Park
Keyword(s):  
Image Representation ◽  
Terrain Analysis ◽  
Generative Adversarial Network ◽  
Training Strategy ◽  
Adversarial Network ◽  
Image Translation ◽  
Successful Approach ◽  
Realistic Image ◽  
Potential Impact ◽  
Realistic Images

Image-to-image translation based on deep learning has attracted interest in the robotics and vision community because of its potential impact on terrain analysis and image representation, interpretation, modification, and enhancement. Currently, the most successful approach for generating a translated image is a conditional generative adversarial network (cGAN) for training an autoencoder with skip connections. Despite its impressive performance, it has low accuracy and a lack of consistency; further, its training is imbalanced. This paper proposes a balanced training strategy for image-to-image translation, resulting in an accurate and consistent network. The proposed approach uses two generators and a single discriminator. The generators translate images from one domain to another. The discriminator takes the input of three different configurations and guides both the generators to generate realistic images in their corresponding domains while ensuring high accuracy and consistency. Experiments are conducted on different datasets. In particular, the proposed approach outperforms the cGAN in realistic image translation in terms of accuracy and consistency in training.

Semi Supervised Generative Adversarial Network for Automated Glaucoma Diagnosis with Stacked Discriminator Models

2021 ◽  
Vol 11 (5) ◽  
pp. 1334-1340
Author(s):  
K. Gokul Kannan ◽  
T. R. Ganesh Babu
Keyword(s):  
Network Architecture ◽  
Super Resolution ◽  
Unsupervised Classification ◽  
Learning Task ◽  
Generative Models ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Glaucoma Diagnosis ◽  
Image Translation

Generative Adversarial Network (GAN) is neural network architecture, widely used in many computer vision applications such as super-resolution image generation, art creation and image to image translation. A conventional GAN model consists of two sub-models; generative model and discriminative model. The former one generates new samples based on an unsupervised learning task, and the later one classifies them into real or fake. Though GAN is most commonly used for training generative models, it can be used for developing a classifier model. The main objective is to extend the effectiveness of GAN into semi-supervised learning, i.e., for the classification of fundus images to diagnose glaucoma. The discriminator model in the conventional GAN is improved via transfer learning to predict n + 1 classes by training the model for both supervised classification (n classes) and unsupervised classification (fake or real). Both models share all feature extraction layers and differ in the output layers. Thus any update in one of the model will impact both models. Results show that the semi-supervised GAN performs well than a standalone Convolution Neural Networks (CNNs) model.

scholarly journals Seg2pix: Few Shot Training Line Art Colorization with Segmented Image Data

2021 ◽  
Vol 11 (4) ◽  
pp. 1464
Author(s):  
Chang Wook Seo ◽  
Yongduek Seo
Keyword(s):  
Image Data ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Training Data ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Segmented Image ◽  
Image Translation ◽  
Segmentation Image

There are various challenging issues in automating line art colorization. In this paper, we propose a GAN approach incorporating semantic segmentation image data. Our GAN-based method, named Seg2pix, can automatically generate high quality colorized images, aiming at computerizing one of the most tedious and repetitive jobs performed by coloring workers in the webtoon industry. The network structure of Seg2pix is mostly a modification of the architecture of Pix2pix, which is a convolution-based generative adversarial network for image-to-image translation. Through this method, we can generate high quality colorized images of a particular character with only a few training data. Seg2pix is designed to reproduce a segmented image, which becomes the suggestion data for line art colorization. The segmented image is automatically generated through a generative network with a line art image and a segmentation ground truth. In the next step, this generative network creates a colorized image from the line art and segmented image, which is generated from the former step of the generative network. To summarize, only one line art image is required for testing the generative model, and an original colorized image and segmented image are additionally required as the ground truth for training the model. These generations of the segmented image and colorized image proceed by an end-to-end method sharing the same loss functions. By using this method, we produce better qualitative results for automatic colorization of a particular character’s line art. This improvement can also be measured by quantitative results with Learned Perceptual Image Patch Similarity (LPIPS) comparison. We believe this may help artists exercise their creative expertise mainly in the area where computerization is not yet capable.

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 Res-WGAN: Image Classification for Plant Small-scale Datasets

2020 ◽  
Author(s):  
Mi Jiaqi ◽  
Hao Xia ◽  
Yang Si ◽  
Gao Wanlin ◽  
Li Minzan ◽  
...  
Keyword(s):  
Deep Learning ◽  
User Study ◽  
Original Data ◽  
Rare Plants ◽  
Small Scale ◽  
Generative Adversarial Network ◽  
Generalization Ability ◽  
Adversarial Network ◽  
Plant Samples ◽  
Basic Network

Abstract Background: The artificial identification of rare plants is always a challenging problem in plant taxonomy. Although the convolutional neural network (CNN) in the deep learning method can better realize the automatic classification of plant samples through training, the model accuracy is difficult to reach the human eye discrimination due to the quantitative limit of training samples. Thus, effective data enhancement is vital to improve the generalization ability and robustness of deep learning models, especially for plant small-scale data classification task. Different from traditional methods, the Generative adversarial network (GAN) mimics original data distribution and produces new samples with similar features which can help classifiers equip with extraordinary generalization ability. It has not been studied that data enhancement for plant samples’ characteristics with GAN since sliced bread. Result: In this study, we present a novel GAN model named as Residual Wasserstein GAN (Res-WGAN) for data enhancement. To further adapt to plant small-scale datasets, residual blocks were introduced into the classic WGAN-GP as the basic network unit. These blocks enrich the presentation skills and sustained parameters unchanged simultaneously. Moreover, we enforce the idea from SRGAN to take content loss into a final function, which guarantes the similarity between generated samples and original samples in high-dimensional features. Benefiting from these improvements, the proposed Res-WGAN expanded original datasets efficiently. We test it on the ResNet and the experimental results show that new datasets combined transfer learning significantly promoted the accuracy of classification, especially at testing data. To illustrate the generalization of the model, more particular small datasets are applied for expansion and classification in this paper. Conclusions: Our works report competitive accuracy results than other data enhancement methods, and user study confirms it’s an ideal alternative strategy for small-scale plant datasets enhancement. Developing robust and effective small-scale plants classification method to replace expert testimony, is highly relevant for agricultural automation development.

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