ArtGAN: Artwork synthesis with conditional categorical GANs

Tan, Wei; Chan, Chee Seng; Aguirre, Hernán; Tanaka, Kiyoshi

doi:10.1109/icip.2017.8296985

Cited by 105 publications

(42 citation statements)

References 3 publications

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“…Over the recent years, Deep Learning (DL) [1] has had a tremendous impact on various fields in science. It has lead to significant improvements in speech recognition [2] and image recognition [3], it is able to train artificial agents that beat human players in Go [4] and ATARI games [5], and it creates artistic new images [6,7] and music [8]. Many of these tasks were considered to be impossible to be solved by computers before the advent of deep learning, even in science fiction literature.…”

Section: Introductionmentioning

confidence: 99%

A gentle introduction to deep learning in medical image processing

Maier

Syben

Lasser

et al. 2019

Zeitschrift für Medizinische Physik

441

224

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This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Obviously medical image processing is one of these areas which has been largely affected by this rapid progress, in particular in image detection and recognition, image segmentation, image registration, and computer-aided diagnosis. There are also recent trends in physical simulation, modelling, and reconstruction that have led to astonishing results. Yet, some of these approaches neglect prior knowledge and hence bear the risk of producing implausible results. These apparent weaknesses highlight current limitations of deep learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future.

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Section: Introductionmentioning

confidence: 99%

A gentle introduction to deep learning in medical image processing

Maier

Syben

Lasser

et al. 2019

Zeitschrift für Medizinische Physik

441

224

View full text Add to dashboard Cite

show abstract

“…Unlike standard images like faces or cars, portraits are often open to massive amounts of interpretation that artists fill in. Using a dataset of precompiled artwork with different genres and labels, the authors input a randomized label corresponding to an art form, and then calculate the image loss based upon how strongly the image deviates from the rest of the training set images of that particular style [36]. Additionally, they note that the reconstruction aspect of decoding encoded images greatly improves the accuracy and efficiency of this hybrid GAN architecture, rather than having two separate, computationally expensive generator and discriminator networks in a minimax game.…”

Section: Applications Of Gans For Image Generationmentioning

confidence: 99%

Realistic River Image Synthesis using Deep Generative Adversarial Networks

Gautam¹,

Sit²,

Demir³

2020

Preprint

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In this paper, we investigate an application of image generation for river satellite imagery. Specifically, we propose a generative adversarial network (GAN) model capable of generating high-resolution and realistic river images that can be used to support models in surface water estimation, river meandering, wetland loss and other hydrological research studies. First, we summarized an augmented, diverse repository of overhead river images to be used in training. Second, we incorporate the Progressive Growing GAN (PGGAN), a network architecture that iteratively trains smaller-resolution GANs to gradually build up to a very high resolution, to generate 256x256 river satellite imagery. With conventional GAN architectures, difficulties soon arise in terms of exponential increase of training time and vanishing/exploding gradient issues, which the PGGAN implementation seems to significantly reduce. Our preliminary results show great promise in capturing the detail of river flow and green areas present in river satellite images that can be used for supporting hydroinformatics studies.

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“…For instance, Deep Convolutional GANs (DCGANs) [38] were designed to allow the network to generate data with similar internal structure as training data, improving the quality of the generated images, and Conditional GANs [39] add an additional conditioning variable to both the generator and the discriminator. Based on the previous architectures the concept of GANs has been adopted to solve many computer visions related tasks such as image generation [40,41], image super-resolution [42], unsupervised learning [43], semi-supervised learning [44], and image painting and colorization [45,46].…”

Section: Generative Adversarial Network (Gans)mentioning

confidence: 99%

Siamese-GAN: Learning Invariant Representations for Aerial Vehicle Image Categorization

et al. 2018

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Abstract:In this paper, we present a new algorithm for cross-domain classification in aerial vehicle images based on generative adversarial networks (GANs). The proposed method, called Siamese-GAN, learns invariant feature representations for both labeled and unlabeled images coming from two different domains. To this end, we train in an adversarial manner a Siamese encoder-decoder architecture coupled with a discriminator network. The encoder-decoder network has the task of matching the distributions of both domains in a shared space regularized by the reconstruction ability, while the discriminator seeks to distinguish between them. After this phase, we feed the resulting encoded labeled and unlabeled features to another network composed of two fully-connected layers for training and classification, respectively. Experiments on several cross-domain datasets composed of extremely high resolution (EHR) images acquired by manned/unmanned aerial vehicles (MAV/UAV) over the cities of Vaihingen, Toronto, Potsdam, and Trento are reported and discussed.

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ArtGAN: Artwork synthesis with conditional categorical GANs

Cited by 105 publications

References 3 publications

A gentle introduction to deep learning in medical image processing

A gentle introduction to deep learning in medical image processing

Realistic River Image Synthesis using Deep Generative Adversarial Networks

Siamese-GAN: Learning Invariant Representations for Aerial Vehicle Image Categorization

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