Yang Q, Yan P, Zhang Y, Yu H, Shi Y, Mou X, Kalra MK, Wang G (2017) Low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss. arXiv: 170800961 69. The repository contains codes for quantum Wasserstein GAN framework and its applications proposed in the following reference: Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, and Xiaodi Wu, Quantum Wasserstein Generative Adversarial Networks, NeurIPS 2019. Jul 14, 2019 · The Wasserstein Generative Adversarial Network, or Wasserstein GAN, is an extension to the generative adversarial network that both improves the stability when training the model and provides a loss function that correlates with the quality of generated images. Abstract A method to filter private data from public data using generative adversarial networks has been introduced in an article “Generative Adversarial Privacy ... Nov 12, 2018 · Quantum Wasserstein Generative Adversarial Networks. NeurIPS 2019 • yiminghwang/qWGAN. The study of quantum generative models is well-motivated, not only because of its importance in quantum machine learning and quantum chemistry but also because of the perspective of its implementation on near-term quantum machines. Jul 17, 2018 · The speech enhancement based on the generative adversarial network has achieved excellent results with large quantities of data, but performance in the low-data regime and tasks like unseen data learning still lag behind. In this work, we model Wasserstein Conditional Generative Adversarial Network-Gradient Penalty speech enhancement system and introduce the elastic network into the objective ... 3.1.2 Adversarial loss. Inspired by [], we built generative adversarial networks (GANs) to predict whether the input image is a real noise-free image.The noise-free images contain rich high-frequency details, thus the introduction of adversarial loss encourages the generator network to generate texture details to fool the discriminator network. Apr 23, 2018 · A major recent breakthrough in classical machine learning is the notion of generative adversarial training, where the gradients of a discriminator model are used to train a separate generative model. In this work and a companion paper, we extend adversarial training to the quantum domain and show how to construct generative adversarial networks using quantum circuits. Quantum Generative Adversarial Networks [5] mimic the ... Quantum Wasserstein GAN-The adversarial optimization problem for a GAN is for- Generative Adversarial Networks (GANs) Wasserstein Divergence and GANs Relaxed Wasserstein Empirical Results Conclusions Bregman and Wasserstein, with Applications to Generative Adversarial Networks (GANs) and beyond Xin Guo Joint work with Johnny Hong, Tianyi Lin and Nan Yang University of California, Berkeley Dec 23, 2017 · Wasserstein Generative adversarial Networks (WGANs) in Tensorflow ... Wasserstein Generative Adversarial Network ... Quantum computing explained with a deck of cards ... Abstract A method to filter private data from public data using generative adversarial networks has been introduced in an article “Generative Adversarial Privacy ... Jan 25, 2019 · Machine learning is growing ever more sophisticated, thanks to algorithms which pit two artificial intelligences against each other. These algorithms, known as generative adversarial networks (GANs), have already been used to create art, crack encryption codes, and produce uncannily real pictures of faces and animals. Qin and Jiang EURASIP Journal on Wireless Communications and Networking Improved Wasserstein conditional generative adversarial network speech enhancement Shan Qin 0 0 Beijing University of Posts and Telecommunications , Haidian District, Beijing 100000 , China The speech enhancement based on the generative adversarial network has achieved excellent results with large quantities of data, but ... Wasserstein Generative Adversarial Networks Martin Arjovsky1 Soumith Chintala2 L´eon Bottou 1 2 Abstract We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning The GAN training procedure consists of defining a game between two adversarial networks: a generator network G maps a source of noise to the input space, and a discriminator network D distinguishes whether a sample originated from the true data distribution or from the generative data distribution. Tongyang Li is a third-year doctoral student in computer science. His adviser is Andrew Childs. Tongyang held a QuICS Lanczos Graduate Fellowship 2015-2017. Apr 23, 2018 · A major recent breakthrough in classical machine learning is the notion of generative adversarial training, where the gradients of a discriminator model are used to train a separate generative model. In this work and a companion paper, we extend adversarial training to the quantum domain and show how to construct generative adversarial networks using quantum circuits. We took an improved version of the original vanilla GAN, Wasserstein’s GAN with Gradient Penalty (you can read the motivation behind it here) originally programmed to generate pictures and modified its entrails (the discriminator and generator networks) to generate synthetic financial universes instead. Jul 17, 2018 · In this work, we model Wasserstein Conditional Generative Adversarial Network-Gradient Penalty speech enhancement system and introduce the elastic network into the objective function to simplify and improve the performance of the model in low-resource data environment. Generative Adversarial Nets (GAN) [1], as one of the generative approach, is the most popular neural networks. It can be used in a variety of applications, including image synthesis, semantic image We developed a new class of physics-informed generative adversarial networks (PI-GANs) to solve in a unified manner forward, inverse and mixed stochastic problems based on a limited number of scattered measurements. Typical generative models include probabilistic graphical models such as the Bayesian nets and the Markov random fields (19, 20, 22), and generative neural networks such as the Boltzmann machines, the deep belief nets, and the generative adversarial networks (21). design a framework for using sampling from a quantum annealer in generative adversarial networks, which may lead to architectures that encourage convergence and decrease mode collapse. Outline First, there is a short section on the background of gans, quantum annealing and Boltzmann machines. Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks Chin-Cheng Hsu1, Hsin-Te Hwang1, Yi-Chiao Wu1, Yu Tsao2, and Hsin-Min Wang1 The repository contains codes for quantum Wasserstein GAN framework and its applications proposed in the following reference: Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, and Xiaodi Wu, Quantum Wasserstein Generative Adversarial Networks, NeurIPS 2019. One of the most promising ways to observe the Universe is by detecting the 21cm emission from cosmic neutral hydrogen (HI) through radio-telescopes. Those observations can shed light on fundamental astrophysical questions only if accurate theoretical predictions are available. In order to maximize the scientific return of these surveys, those predictions need to include different observables ... Qin and Jiang EURASIP Journal on Wireless Communications and Networking Improved Wasserstein conditional generative adversarial network speech enhancement Shan Qin 0 0 Beijing University of Posts and Telecommunications , Haidian District, Beijing 100000 , China The speech enhancement based on the generative adversarial network has achieved excellent results with large quantities of data, but ... Jan 25, 2019 · Machine learning is growing ever more sophisticated, thanks to algorithms which pit two artificial intelligences against each other. These algorithms, known as generative adversarial networks (GANs), have already been used to create art, crack encryption codes, and produce uncannily real pictures of faces and animals. Apr 23, 2018 · A major recent breakthrough in classical machine learning is the notion of generative adversarial training, where the gradients of a discriminator model are used to train a separate generative model. In this work and a companion paper, we extend adversarial training to the quantum domain and show how to construct generative adversarial networks using quantum circuits. Introduction to Generative Models (and GANs) Haoqiang Fan [email protected] April. 2019 Figures adapted from NIPS 2016 Tutorial Generative Adversarial Networks Generative adversarial networks (GANs) [19] represent a power tool of training deep generative models, which have a profound impact on machine learning. In GANs, a generator tries to generate fake samples resembling the true data, while a discriminator tries to discriminate between the true and the fake data. utilizes a Generative Adversarial Network (GAN) to improve an existing model for Image Super Resolution, we propose a modified Wasserstein GAN architecture to enhance a model for audio super-resolution, ASRNet, introduced by [2]. Generative Adversarial Networks (GANs) Wasserstein Divergence and GANs Relaxed Wasserstein Empirical Results Conclusions Bregman and Wasserstein, with Applications to Generative Adversarial Networks (GANs) and beyond Xin Guo Joint work with Johnny Hong, Tianyi Lin and Nan Yang University of California, Berkeley Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks. The Annual Conference of the International Speech Communication Association (INTERSPEECH) , 2017. In this work and a companion paper, we extend adversarial training to the quantum domain and show how to construct generative adversarial networks using quantum circuits. Furthermore, we also show how to compute gradients -- a key element in generative adversarial network training -- using another quantum circuit.

Apr 09, 2019 · Open Questions about Generative Adversarial Networks. What we’d like to find out about GANs that we don’t know yet. Problem 1 What are the trade-offs between GANs and other generative models?