scholarly journals Modelling and forecasting art movements with CGANs

2020 ◽  
Vol 7 (4) ◽  
pp. 191569
Author(s):  
Edoardo Lisi ◽  
Mohammad Malekzadeh ◽  
Hamed Haddadi ◽  
F. Din-Houn Lau ◽  
Seth Flaxman
Keyword(s):  
Generative Adversarial Networks ◽  
Vector Autoregressive ◽  
Adversarial Networks ◽  
Latent Distribution ◽  
Small Set ◽  
One Step ◽  
Temporal Aspect ◽  
Novel Method ◽  
The Mean ◽  
Modelling And Forecasting

Conditional generative adversarial networks (CGANs) are a recent and popular method for generating samples from a probability distribution conditioned on latent information. The latent information often comes in the form of a discrete label from a small set. We propose a novel method for training CGANs which allows us to condition on a sequence of continuous latent distributions f (1) , …, f ( K ) . This training allows CGANs to generate samples from a sequence of distributions. We apply our method to paintings from a sequence of artistic movements, where each movement is considered to be its own distribution. Exploiting the temporal aspect of the data, a vector autoregressive (VAR) model is fitted to the means of the latent distributions that we learn, and used for one-step-ahead forecasting, to predict the latent distribution of a future art movement f ( K +1) . Realizations from this distribution can be used by the CGAN to generate ‘future’ paintings. In experiments, this novel methodology generates accurate predictions of the evolution of art. The training set consists of a large dataset of past paintings. While there is no agreement on exactly what current art period we find ourselves in, we test on plausible candidate sets of present art, and show that the mean distance to our predictions is small.

scholarly journals Dynamics of Fourier Modes in Torus Generative Adversarial Networks

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 325
Author(s):  
Ángel González-Prieto ◽  
Alberto Mozo ◽  
Edgar Talavera ◽  
Sandra Gómez-Canaval
Keyword(s):  
Fourier Series ◽  
Generative Adversarial Networks ◽  
Learning Models ◽  
Training Process ◽  
Small Perturbations ◽  
Adversarial Networks ◽  
Novel Method ◽  
Truncated Fourier Series ◽  
Real Flow ◽  
Machine Learning Models

Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating fully synthetic samples of a desired phenomenon with a high resolution. Despite their success, the training process of a GAN is highly unstable, and typically, it is necessary to implement several accessory heuristics to the networks to reach acceptable convergence of the model. In this paper, we introduce a novel method to analyze the convergence and stability in the training of generative adversarial networks. For this purpose, we propose to decompose the objective function of the adversary min–max game defining a periodic GAN into its Fourier series. By studying the dynamics of the truncated Fourier series for the continuous alternating gradient descend algorithm, we are able to approximate the real flow and to identify the main features of the convergence of GAN. This approach is confirmed empirically by studying the training flow in a 2-parametric GAN, aiming to generate an unknown exponential distribution. As a by-product, we show that convergent orbits in GANs are small perturbations of periodic orbits so the Nash equillibria are spiral attractors. This theoretically justifies the slow and unstable training observed in GANs.

Cast suppression in radiographs by generative adversarial networks

2021 ◽  
Author(s):  
Franko Hržić ◽  
Ivana Žužić ◽  
Sebastian Tschauner ◽  
Ivan Štajduhar
Keyword(s):  
Software Tool ◽  
Generative Adversarial Networks ◽  
X Rays ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
The Mean ◽  
Radiology Image

Abstract Injured extremities commonly need to be immobilized by casts to allow proper healing. We propose a method to suppress cast superimpositions in pediatric wrist radiographs based on the cycle generative adversarial network (CycleGAN) model. We retrospectively reviewed unpaired pediatric wrist radiographs (n = 9672) and sampled them into 2 equal groups, with and without cast. The test subset consisted of 718 radiographs with cast. We evaluated different quadratic input sizes (256, 512, and 1024 pixels) for U-Net and ResNet-based CycleGAN architectures in cast suppression, quantitatively and qualitatively. The mean age was 11 ± 3 years in images containing cast (n = 4836), and 11 ± 4 years in castless samples (n = 4836). A total of 5956 X-rays had been done in males and 3716 in females. A U-Net 512 CycleGAN performed best (P ≤ .001). CycleGAN models successfully suppressed casts in pediatric wrist radiographs, allowing the development of a related software tool for radiology image viewers.

scholarly journals Evolving optimum camouflage with Generative Adversarial Networks

2018 ◽  
Author(s):  
Laszlo Talas ◽  
John G. Fennell ◽  
Karin Kjernsmo ◽  
Innes C. Cuthill ◽  
Nicholas E. Scott-Samuel ◽  
...  
Keyword(s):  
Arms Race ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Novel Method ◽  
Multiple Environments ◽  
Evolutionary Arms Race

AbstractWe describe a novel method to exploit Generative Adversarial Networks to simulate an evolutionary arms race between the camouflage of a synthetic prey and its predator. Patterns evolved using our methods are shown to provide progressively more effective concealment and outperform two recognised camouflage techniques. The method will be invaluable, particularly for biologists, for rapidly developing and testing optimal camouflage or signalling patterns in multiple environments.

scholarly journals FT-GAN: Face Transformation with Key Points Alignment for Pose-Invariant Face Recognition

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 807 ◽  
Author(s):  
Weiwei Zhuang ◽  
Liang Chen ◽  
Chaoqun Hong ◽  
Yuxin Liang ◽  
Keshou Wu
Keyword(s):  
Face Recognition ◽  
Generative Adversarial Networks ◽  
Facial Features ◽  
Adversarial Networks ◽  
Key Points ◽  
In The Wild ◽  
Novel Method ◽  
Face Synthesis ◽  
Unconstrained Face

Face recognition has been comprehensively studied. However, face recognition in the wild still suffers from unconstrained face directions. Frontal face synthesis is a popular solution, but some facial features are missed after synthesis. This paper presents a novel method for pose-invariant face recognition. It is based on face transformation with key points alignment based on generative adversarial networks (FT-GAN). In this method, we introduce CycleGAN for pixel transformation to achieve coarse face transformation results, and these results are refined by key point alignment. In this way, frontal face synthesis is modeled as a two-task process. The results of comprehensive experiments show the effectiveness of FT-GAN.

scholarly journals Semantic Predictive Coding with Arbitrated Generative Adversarial Networks

2020 ◽  
Vol 2 (3) ◽  
pp. 307-326
Author(s):  
Radamanthys Stivaktakis ◽  
Grigorios Tsagkatakis ◽  
Panagiotis Tsakalides
Keyword(s):  
Semantic Information ◽  
Predictive Coding ◽  
Generative Adversarial Networks ◽  
Modeling Framework ◽  
Principal Role ◽  
Adversarial Networks ◽  
Alternative Approach ◽  
Temporal Aspect ◽  
Spatio Temporal ◽  
Experimental Findings

In spatio-temporal predictive coding problems, like next-frame prediction in video, determining the content of plausible future frames is primarily based on the image dynamics of previous frames. We establish an alternative approach based on their underlying semantic information when considering data that do not necessarily incorporate a temporal aspect, but instead they comply with some form of associative ordering. In this work, we introduce the notion of semantic predictive coding by proposing a novel generative adversarial modeling framework which incorporates the arbiter classifier as a new component. While the generator is primarily tasked with the anticipation of possible next frames, the arbiter’s principal role is the assessment of their credibility. Taking into account that the denotative meaning of each forthcoming element can be encapsulated in a generic label descriptive of its content, a classification loss is introduced along with the adversarial loss. As supported by our experimental findings in a next-digit and a next-letter scenario, the utilization of the arbiter not only results in an enhanced GAN performance, but it also broadens the network’s creative capabilities in terms of the diversity of the generated symbols.

scholarly journals GENERATIVE ADVERSARIAL NETWORKS AS A NOVEL APPROACH FOR TECTONIC FAULT AND FRACTURE EXTRACTION IN HIGH-RESOLUTION SATELLITE AND AIRBORNE OPTICAL IMAGES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1219-1227
Author(s):  
B. Jafrasteh ◽  
I. Manighetti ◽  
J. Zerubia
Keyword(s):  
Ground Truth ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Convolutional Networks ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Optical Images ◽  
Novel Approach ◽  
Ground Truth Image ◽  
Novel Method

Abstract. We develop a novel method based on Deep Convolutional Networks (DCN) to automate the identification and mapping of fracture and fault traces in optical images. The method employs two DCNs in a two players game: a first network, called Generator, learns to segment images to make them resembling the ground truth; a second network, called Discriminator, measures the differences between the ground truth image and each segmented image and sends its score feedback to the Generator; based on these scores, the Generator improves its segmentation progressively. As we condition both networks to the ground truth images, the method is called Conditional Generative Adversarial Network (CGAN). We propose a new loss function for both the Generator and the Discriminator networks, to improve their accuracy. Using two criteria and a manually annotated optical image, we compare the generalization performance of the proposed method to that of a classical DCN architecture, U-net. The comparison demonstrates the suitability of the proposed CGAN architecture. Further work is however needed to improve its efficiency.

scholarly journals Exploiting Images for Video Recognition with Hierarchical Generative Adversarial Networks

2018 ◽  
Author(s):  
Feiwu Yu ◽  
Xinxiao Wu ◽  
Yuchao Sun ◽  
Lixin Duan
Keyword(s):  
Domain Adaptation ◽  
Feature Representation ◽  
Generative Adversarial Networks ◽  
Target Domain ◽  
Feature Representations ◽  
Adversarial Networks ◽  
Video Recognition ◽  
Novel Method ◽  
High Level ◽  
Heterogeneous Feature

Existing deep learning methods of video recognition usually require a large number of labeled videos for training. But for a new task, videos are often unlabeled and it is also time-consuming and labor-intensive to annotate them. Instead of human annotation, we try to make use of existing fully labeled images to help recognize those videos. However, due to the problem of domain shifts and heterogeneous feature representations, the performance of classifiers trained on images may be dramatically degraded for video recognition tasks. In this paper, we propose a novel method, called Hierarchical Generative Adversarial Networks (HiGAN), to enhance recognition in videos (i.e., target domain) by transferring knowledge from images (i.e., source domain). The HiGAN model consists of a \emph{low-level} conditional GAN and a \emph{high-level} conditional GAN. By taking advantage of these two-level adversarial learning, our method is capable of learning a domain-invariant feature representation of source images and target videos. Comprehensive experiments on two challenging video recognition datasets (i.e. UCF101 and HMDB51) demonstrate the effectiveness of the proposed method when compared with the existing state-of-the-art domain adaptation methods.

Content-Based Attention Network for Person Image Generation

2020 ◽  
Vol 29 (15) ◽  
pp. 2050250
Author(s):  
Xiongfei Liu ◽  
Bengao Li ◽  
Xin Chen ◽  
Haiyan Zhang ◽  
Shu Zhan
Keyword(s):  
Major Part ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
Attention Network ◽  
Adversarial Networks ◽  
Proposed Model ◽  
Novel Method

This paper proposes a novel method for person image generation with arbitrary target pose. Given a person image and an arbitrary target pose, our proposed model can synthesize images with the same person but different poses. The Generative Adversarial Networks (GANs) are the major part of the proposed model. Different from the traditional GANs, we add attention mechanism to the generator in order to generate realistic-looking images, we also use content reconstruction with a pretrained VGG16 Net to keep the content consistency between generated images and target images. Furthermore, we test our model on DeepFashion and Market-1501 datasets. The experimental results show that the proposed network performs favorably against state-of-the-art methods.

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