In this pioneering research paper, we present a groundbreaking exploration into the synergistic fusion of classical and quantum computing paradigms within the realm of Generative Adversarial Networks (GANs). Our objective is to seamlessly integrate quantum computational elements into the conventional GAN architecture, thereby unlocking novel pathways for enhanced training processes.
Drawing inspiration from the inherent capabilities of quantum bits (qubits), we delve into the incorporation of quantum data representation methodologies within the GAN framework. By capitalizing on the unique quantum features, we aim to accelerate the training process of GANs, offering a fresh perspective on the optimization of generative models.
Our investigation deals with theoretical considerations and evaluates the potential quantum advantages that may manifest in terms of training efficiency and generative quality. We confront the challenges inherent in the quantum-classical amalgamation, addressing issues related to quantum hardware constraints, error correction mechanisms, and scalability considerations. This research is positioned at the forefront of quantum-enhanced machine learning, presenting a critical stride towards harnessing the computational power of quantum systems to expedite the training of Generative Adversarial Networks. Through our comprehensive examination of the interface between classical and quantum realms, we aim to uncover transformative insights that will propel the field forward, fostering innovation and advancing the frontier of quantum machine learning.
Tsinghua University（清华大学）；Beijing Innovation Center for Future Chip（北京未来芯片创新中心）
Generative adversarial networks have achieved remarkable performance on various tasks but suffer from training instability. Despite many training strategies proposed to improve training stability, this issue remains as a challenge. In this paper, we investigate the training instability from the perspective of adversarial samples and reveal that adversarial training on fake samples is implemented in vanilla GANs, but adversarial training on real samples has long been overlooked. Consequently, the discriminator is extremely vulnerable to adversarial perturbation and the gradient given by the dis…
Generative Adversarial Networks Bridging Art and Machine Intelligence
Imperial College London（帝国理工学院）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Indiana University（印第安纳大学）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Georgia Institute of Technology（佐治亚理工学院）；Kyoto University（京都大学）；AppCubic；Rutgers University（罗格斯大学）；Purdue University（普渡大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；National Taiwan Normal University（台湾师范大学）；University of Hawaii（夏威夷大学）；Hong Kong University of Science and Technology（香港科技大学）；School of Visual Arts（视觉艺术学院）；Emory University（埃默里大学）；Aarhus University（奥胡斯大学）；Zhejiang University（浙江大学）；University of Edinburgh（爱丁堡大学）；National Tsing Hua University（国立清华大学）；University of Manchester（曼彻斯特大学）；Pingtan Research Institute of Xiamen University（厦门大学平潭研究院）；University of Liverpool（利物浦大学）；JTB Technology Corp.（JTB科技公司）
Since their proposal in the 2014 paper by Ian Goodfellow, there has been an explosion of research into the area of Generative Adversarial Networks. While they have been utilised in many fields, the realm of malware research is a problem space in which GANs have taken root. From balancing datasets to creating unseen examples in rare classes, GAN models offer extensive opportunities for application. This paper surveys the current research and literature for the use of Generative Adversarial Networks in the malware problem space. This is done with the hope that the reader may be able to gain an o…
Tailoring Generative Adversarial Networks for Smooth Airfoil Design
Institute of High Performance Computing（高性能计算研究所）；Agency for Science, Technology and Research（新加坡科技研究局）；School of Continuing and Lifelong Education（继续教育与终身教育学院）；National University of Singapore（新加坡国立大学）
Generative adversarial networks (GANs) have enjoyed much success in learning high-dimensional distributions. Learning objectives approximately minimize an $f$-divergence ($f$-GANs) or an integral probability metric (Wasserstein GANs) between the model and the data distribution using a discriminator. Wasserstein GANs enjoy superior empirical performance, but in $f$-GANs the discriminator can be interpreted as a density ratio estimator which is necessary in some GAN applications. In this paper, we bridge the gap between $f$-GANs and Wasserstein GANs (WGANs). First, we list two constraints over v…
Predictive process monitoring aims to predict future characteristics of an ongoing process case, such as case outcome or remaining timestamp. Recently, several predictive process monitoring methods based on deep learning such as Long Short-Term Memory or Convolutional Neural Network have been proposed to address the problem of next event prediction. However, due to insufficient training data or sub-optimal network configuration and architecture, these approaches do not generalize well the problem at hand. This paper proposes a novel adversarial training framework to address this shortcoming, b…
A Synergistic Framework Leveraging Autoencoders and Generative Adversarial Networks for the Synthesis of Computational Fluid Dynamics Results in Aerofoil Aerodynamics
The University of Tokyo（东京大学）；LPIXEL Inc.（LPIXEL公司）；National Institute of Informatics（国立信息学研究所）；University of Cambridge（剑桥大学）；Keio University（庆应义塾大学）
Despite the success of Convolutional Neural Network-based Computer-Aided Diagnosis research, its clinical applications remain challenging. Accordingly, developing medical Artificial Intelligence (AI) fitting into a clinical environment requires identifying/bridging the gap between AI and Healthcare sides. Since the biggest problem in Medical Imaging lies in data paucity, confirming the clinical relevance for diagnosis of research-proven image augmentation techniques is essential. Therefore, we hold a clinically valuable AI-envisioning workshop among Japanese Medical Imaging experts, physicians…
The University of Tokyo（东京大学）；CyberAgent, Inc.（赛博艾金特公司）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Preferred Networks, Inc.（优选网络公司）
This paper provides a unified perspective for the Kullback-Leibler (KL)-divergence and the integral probability metrics (IPMs) from the perspective of maximum likelihood density-ratio estimation (DRE). Both the KL-divergence and the IPMs are widely used in various fields in applications such as generative modeling. However, a unified understanding of these concepts has still been unexplored. In this paper, we show that the KL-divergence and the IPMs can be represented as maximal likelihoods differing only by sampling schemes, and use this result to derive a unified form of the IPMs and a relax…
Generative adversarial networks (GANs) are a framework that learns a generative distribution through adversarial training. Recently, their class-conditional extensions (e.g., conditional GAN (cGAN) and auxiliary classifier GAN (AC-GAN)) have attracted much attention owing to their ability to learn the disentangled representations and to improve the training stability. However, their training requires the availability of large-scale accurate class-labeled data, which are often laborious or impractical to collect in a real-world scenario. To remedy this, we propose a novel family of GANs called …
Network embedding has become a hot research topic recently which can provide low-dimensional feature representations for many machine learning applications. Current work focuses on either (1) whether the embedding is designed as an unsupervised learning task by explicitly preserving the structural connectivity in the network, or (2) whether the embedding is a by-product during the supervised learning of a specific discriminative task in a deep neural network. In this paper, we focus on bridging the gap of the two lines of the research. We propose to adapt the Generative Adversarial model to pe…
Institute of Computer Science and Technology, Peking University（北京大学计算机科学技术研究所）
It is known that the inconsistent distribution and representation of different modalities, such as image and text, cause the heterogeneity gap that makes it challenging to correlate such heterogeneous data. Generative adversarial networks (GANs) have shown its strong ability of modeling data distribution and learning discriminative representation, existing GANs-based works mainly focus on generative problem to generate new data. We have different goal, aim to correlate heterogeneous data, by utilizing the power of GANs to model cross-modal joint distribution. Thus, we propose Cross-modal GANs …
University of California, Los Angeles（加州大学洛杉矶分校）；Google Brain（谷歌大脑）
Conditional GANs are at the forefront of natural image synthesis. The main drawback of such models is the necessity for labeled data. In this work we exploit two popular unsupervised learning techniques, adversarial training and self-supervision, and take a step towards bridging the gap between conditional and unconditional GANs. In particular, we allow the networks to collaborate on the task of representation learning, while being adversarial with respect to the classic GAN game. The role of self-supervision is to encourage the discriminator to learn meaningful feature representations which a…
The University of Iowa（爱荷华大学）；IBM T. J. Watson Research Center（IBM托马斯·J·沃森研究中心）
Adaptive gradient algorithms perform gradient-based updates using the history of gradients and are ubiquitous in training deep neural networks. While adaptive gradient methods theory is well understood for minimization problems, the underlying factors driving their empirical success in min-max problems such as GANs remain unclear. In this paper, we aim at bridging this gap from both theoretical and empirical perspectives. First, we analyze a variant of Optimistic Stochastic Gradient (OSG) proposed in~\citep{daskalakis2017training} for solving a class of non-convex non-concave min-max problem a…
Center for Quantum Information, IIIS, Tsinghua University（清华大学交叉信息研究院量子信息中心）；Max-Planck-Institut für Quantenoptik（马克斯·普朗克量子光学研究所）
Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of machine learning approaches in adversarial settings and developing techniques accordingly to make learning robust to adversarial manipulations. It plays a vital role in various machine learning applications and has attracted tremendous attention across different communities recently. In this paper, we explore different adversarial scenarios in the context of quantum machine learning. We find that, similar to traditional classifiers based on classical neural networks, quantum learning systems are likew…
Modern experiments using nanoscale devices come ever closer to bridging the divide between the quantum and classical realms, bringing experimental tests of objective collapse theories that propose alterations to Schrödinger's equation within reach. Such objective collapse theories aim to explain the emergence of classical dynamics in the thermodynamic limit and hence resolve the inconsistency that exists within the axioms of quantum mechanics. Here, we show that requiring the emergence of Born's rule for relative frequencies of measurement outcomes without imposing them as part of any axiom, i…
