A Generative Adversarial Network (GAN) with generator $G$ trained to model the prior of images has been shown to perform better than sparsity-based regularizers in ill-posed inverse problems. Here, we propose a new method of deploying a GAN-based prior to solve linear inverse problems using projected gradient descent (PGD). Our method learns a network-based projector for use in the PGD algorithm, eliminating expensive computation of the Jacobian of $G$. Experiments show that our approach provides a speed-up of $60\text{-}80\times$ over earlier GAN-based recovery methods along with better accuracy. Our main theoretical result is that if the measurement matrix is moderately conditioned on the manifold range($G$) and the projector is $δ$-approximate, then the algorithm is guaranteed to reach $O(δ)$ reconstruction error in $O(log(1/δ))$ steps in the low noise regime. Additionally, we propose a fast method to design such measurement matrices for a given $G$. Extensive experiments demonstrate the efficacy of this method by requiring $5\text{-}10\times$ fewer measurements than random Gaussian measurement matrices for comparable recovery performance. Because the learning of the GAN and projector is decoupled from the measurement operator, our GAN-based projector and recovery algorithm are applicable without retraining to all linear inverse problems, as confirmed by experiments on compressed sensing, super-resolution, and inpainting.
Generative adversarial networks (GANs) transform latent vectors into visually plausible images. It is generally thought that the original GAN formulation gives no out-of-the-box method to reverse the mapping, projecting images back into latent space. We introduce a simple, gradient-based technique called stochastic clipping. In experiments, for images generated by the GAN, we precisely recover their latent vector pre-images 100% of the time. Additional experiments demonstrate that this method is robust to noise. Finally, we show that even for unseen images, our method appears to recover unique…
The Generator of a Generative Adversarial Network (GAN) is trained to transform latent vectors drawn from a prior distribution into realistic looking photos. These latent vectors have been shown to encode information about the content of their corresponding images. Projecting input images onto the latent space of a GAN is non-trivial, but previous work has successfully performed this task for latent spaces with a uniform prior. We extend these techniques to latent spaces with a Gaussian prior, and demonstrate our technique's effectiveness.
Normandie Univ（诺曼底大学）；UNICAEN（卡昂大学）；ENSICAEN（卡昂高等工程师学院）；CNRS — UMR GREYC（法国国家科学研究中心 — 灰谐实验室联合研究单位（注：CNRS为法国国家科学研究中心，UMR为联合研究单位缩写，GREYC为实验室名，此处按原文提取机构部分））
State of the art deep generative networks are capable of producing images with such incredible realism that they can be suspected of memorizing training images. It is why it is not uncommon to include visualizations of training set nearest neighbors, to suggest generated images are not simply memorized. We demonstrate this is not sufficient and motivates the need to study memorization/overfitting of deep generators with more scrutiny. This paper addresses this question by i) showing how simple losses are highly effective at reconstructing images for deep generators ii) analyzing the statistics…
Reducing the Representation Error of GAN Image Priors Using the Deep Decoder
Department of Mathematics and Khoury College of Computer Sciences, Northeastern University（数学系和Khoury计算机科学学院，东北大学）；Electrical Engineering and Information Technology, Technical University of Munich（电子工程与信息科技，慕尼黑技术大学）
Single-pixel imaging is a novel imaging scheme that has gained popularity due to its huge computational gain and potential for a low-cost alternative to imaging beyond the visible spectrum. The traditional reconstruction methods struggle to produce a clear recovery when one limits the number of illumination patterns from a spatial light modulator. As a remedy, several deep-learning-based solutions have been proposed which lack good generalization ability due to the architectural setup and loss functions. In this paper, we propose a generative adversarial network-based reconstruction framework …
While energy-based models (EBMs) exhibit a number of desirable properties, training and sampling on high-dimensional datasets remains challenging. Inspired by recent progress on diffusion probabilistic models, we present a diffusion recovery likelihood method to tractably learn and sample from a sequence of EBMs trained on increasingly noisy versions of a dataset. Each EBM is trained with recovery likelihood, which maximizes the conditional probability of the data at a certain noise level given their noisy versions at a higher noise level. Optimizing recovery likelihood is more tractable than …
Computer Science Department, The University of Oxford（牛津大学计算机科学系）
In the last decade, supervised deep learning approaches have been extensively employed in visual odometry (VO) applications, which is not feasible in environments where labelled data is not abundant. On the other hand, unsupervised deep learning approaches for localization and mapping in unknown environments from unlabelled data have received comparatively less attention in VO research. In this study, we propose a generative unsupervised learning framework that predicts 6-DoF pose camera motion and monocular depth map of the scene from unlabelled RGB image sequences, using deep convolutional G…
We present a theoretical analysis of the training process for a single-layer GAN fed by high-dimensional input data. The training dynamics of the proposed model at both microscopic and macroscopic scales can be exactly analyzed in the high-dimensional limit. In particular, we prove that the macroscopic quantities measuring the quality of the training process converge to a deterministic process characterized by an ordinary differential equation (ODE), whereas the microscopic states containing all the detailed weights remain stochastic, whose dynamics can be described by a stochastic differentia…
A conditional Generative Adversarial Network allows for generating samples conditioned on certain external information. Being able to recover latent and conditional vectors from a condi- tional GAN can be potentially valuable in various applications, ranging from image manipulation for entertaining purposes to diagnosis of the neural networks for security purposes. In this work, we show that it is possible to recover both latent and conditional vectors from generated images given the generator of a conditional generative adversarial network. Such a recovery is not trivial due to the often mult…
Technical University of Munich（慕尼黑工业大学）；LMU Klinikum Grosshadern（慕尼黑大学总医院格罗斯哈登分院）
In recent years, Generative Adversarial Networks (GAN) have emerged as a powerful method for learning the mapping from noisy latent spaces to realistic data samples in high-dimensional space. So far, the development and application of GANs have been predominantly focused on spatial data such as images. In this project, we aim at modeling of spatio-temporal sensor data instead, i.e. dynamic data over time. The main goal is to encode temporal data into a global and low-dimensional latent vector that captures the dynamics of the spatio-temporal signal. To this end, we incorporate auto-regressive …
ShanghaiTech University（上海科技大学）；Tencent AI Lab（腾讯人工智能实验室）
We tackle the human motion imitation, appearance transfer, and novel view synthesis within a unified framework, which means that the model once being trained can be used to handle all these tasks. The existing task-specific methods mainly use 2D keypoints (pose) to estimate the human body structure. However, they only expresses the position information with no abilities to characterize the personalized shape of the individual person and model the limbs rotations. In this paper, we propose to use a 3D body mesh recovery module to disentangle the pose and shape, which can not only model the join…
Over the past few years deep learning-based techniques such as Generative Adversarial Networks (GANs) have significantly improved solutions to image super-resolution and image-to-image translation problems. In this paper, we propose a solution to the joint problem of image super-resolution and multi-modality image-to-image translation. The problem can be stated as the recovery of a high-resolution image in a modality, given a low-resolution observation of the same image in an alternative modality. Our paper offers two models to address this problem and will be evaluated on the recovery of high…
Compressed sensing (CS) provides an elegant framework for recovering sparse signals from compressed measurements. For example, CS can exploit the structure of natural images and recover an image from only a few random measurements. CS is flexible and data efficient, but its application has been restricted by the strong assumption of sparsity and costly reconstruction process. A recent approach that combines CS with neural network generators has removed the constraint of sparsity, but reconstruction remains slow. Here we propose a novel framework that significantly improves both the performance…
Institute for Research in Fundamental Sciences (IPM)（基础科学研究所（IPM））；Scuola Internazionale Superiore di Studi Avanzati (SISSA)（国际高等研究院（SISSA））；Institute for Fundamental Physics of the Universe (IFPU)（宇宙基础物理研究所（IFPU））
In this work, we propose a new method to inpaint the CMB signal in regions masked out following a point source extraction process. We adopt a modified Generative Adversarial Network (GAN) and compare different combinations of internal (hyper-)parameters and training strategies. We study the performance using a suitable $\mathcal{C}_r$ variable in order to estimate the performance regarding the CMB power spectrum recovery. We consider a test set where one point source is masked out in each sky patch with a 1.83 $\times$ 1.83 squared degree extension, which, in our gridding, corresponds to 64 $\…
Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI
Surgery, Karolinska Institutet , addressline= Karolinska Universitetssjukhuset Solna (L1:00) , city= Stockholm , postcode= 171 76 , country= Sweden；organization= FLOW, Engineering Mechanics, KTH Royal Institute of Technology , addressline= Osquars Backe 18 , city= Stockholm , postcode= 100 44 , country= Sweden；organization= Department of Radiology；Biomedical Imaging, University of California San Francisco , addressline= 505 Parnassus Avenue , city= San Francisco , postcode= 94143 , state= CA , country= USA；organization= Faculty of Informatics, Telkom University , addressline= Jl.Telekomunikasi No. 1, Terusan Buahbatu , city= Bandung , postcode= 40257 , state= West Java , country= Indonesia；organization= Auckland Bioengineering Institute, University of Auckland , addressline= Bioengineering House, 70 Symonds St , city= Grafton , postcode= 1010 , country= New Zealand；organization= School of Biomedical Engineering \& Imaging Sciences, King's College London , addressline= 1 Lambeth Palace Rd, South Bank , city= London , postcode= SE1 7EU , country= UK；organization= Department of Biomedical Engineering, University of Michigan , addressline= 1107 Carl A. Gerstacker Bldg 2200 Bonisteel Blvd. , city= Ann Arbor , postcode= 48109-2099 , state= MI , country= USA；organization= Department of Physics, University of Greifswald , addressline= Felix-Hausdorff-Str. 6 , city= Greifswald , postcode= 174 89 , country= Germany；organization= Department of Aerospace Engineering, University of Michigan , addressline= 1320 Beal Avenue , city= Ann Arbor , postcode= 48109-2140 , state= MI , country= USA；organization= Department of Neuroradiology, Karolinska University Hospital , addressline= Hälsovägen 13, O42 , city= Stockholm , postcode= 141 86 , country= Sweden；organization= Department of Clinical Physiology, Karolinska University Hospital , addressline= Eugeniavägen 3, A8:01 , city= Solna , postcode= 171 64 , country= Sweden；organization= Institute for Medical Engineering；Science, Massachusetts Institute of Technology , addressline= 45 Carleton St , city= Cambridge , postcode= 02142 , state= MA , country= USA
Deep generative models (e.g. GANs and VAEs) have been developed quite extensively in recent years. Lately, there has been an increased interest in the inversion of such a model, i.e. given a (possibly corrupted) signal, we wish to recover the latent vector that generated it. Building upon sparse representation theory, we define conditions that are applicable to any inversion algorithm (gradient descent, deep encoder, etc.), under which such generative models are invertible with a unique solution. Importantly, the proposed analysis is applicable to any trained model, and does not depend on Gaus…
Imperial College London（伦敦帝国学院）；Loughborough University（拉夫堡大学）
Building a fault-tolerant edge system that can quickly react to node overloads or failures is challenging due to the unreliability of edge devices and the strict service deadlines of modern applications. Moreover, unnecessary task migrations can stress the system network, giving rise to the need for a smart and parsimonious failure recovery scheme. Prior approaches often fail to adapt to highly volatile workloads or accurately detect and diagnose faults for optimal remediation. There is thus a need for a robust and proactive fault-tolerance mechanism to meet service level objectives. In this w…
Boston University（波士顿大学）；Microsoft Research（微软研究院）
Although deep neural networks have achieved great performance on classification tasks, recent studies showed that well trained networks can be fooled by adding subtle noises. This paper introduces a new approach to improve neural network robustness by applying the recovery process on top of the naturally trained classifier. In this approach, images will be intentionally corrupted by some significant operator and then be recovered before passing through the classifiers. SARGAN -- an extension on Generative Adversarial Networks (GAN) is capable of denoising radar signals. This paper will show th…
University of New South Wales（新南威尔士大学）；School of Minerals and Energy Resources Engineering（矿物与能源资源工程学院）
Digital Rock Imaging is constrained by detector hardware, and a trade-off between the image field of view (FOV) and the image resolution must be made. This can be compensated for with super resolution (SR) techniques that take a wide FOV, low resolution (LR) image, and super resolve a high resolution (HR), high FOV image. The Enhanced Deep Super Resolution Generative Adversarial Network (EDSRGAN) is trained on the Deep Learning Digital Rock Super Resolution Dataset, a diverse compilation 12000 of raw and processed uCT images. The network shows comparable performance of 50% to 70% reduction in …
Henry Ford Health System（亨利福特医疗系统）；Wayne State University（韦恩州立大学）
Every year thousands of patients are diagnosed with a glioma, a type of malignant brain tumor. Physicians use MR images as a key tool in the diagnosis and treatment of these patients. Neural networks show great potential to aid physicians in the medical image analysis. This study investigates the use of varying amounts of synthetic brain T1-weighted (T1), post-contrast T1-weighted (T1Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR) MR images created by a generative adversarial network to overcome the lack of annotated medical image data in training separate 2D U-Nets t…
Adversarial patch attacks create adversarial examples by injecting arbitrary distortions within a bounded region of the input to fool deep neural networks (DNNs). These attacks are robust (i.e., physically-realizable) and universally malicious, and hence represent a severe security threat to real-world DNN-based systems. We propose Jujutsu, a two-stage technique to detect and mitigate robust and universal adversarial patch attacks. We first observe that adversarial patches are crafted as localized features that yield large influence on the prediction output, and continue to dominate the predic…
NTT Communication Science Laboratories, NTT Corporation（日本电信电话株式会社通信科学实验室）
In recent text-to-speech synthesis and voice conversion systems, a mel-spectrogram is commonly applied as an intermediate representation, and the necessity for a mel-spectrogram vocoder is increasing. A mel-spectrogram vocoder must solve three inverse problems: recovery of the original-scale magnitude spectrogram, phase reconstruction, and frequency-to-time conversion. A typical convolutional mel-spectrogram vocoder solves these problems jointly and implicitly using a convolutional neural network, including temporal upsampling layers, when directly calculating a raw waveform. Such an approach …
