An approach to utilize recent advances in deep generative models for anomaly detection in a granular (continuous) sense on a real-world image dataset with quality issues is detailed using recent normalizing flow models, with implications in many other applications/domains/data types. The approach is completely unsupervised (no annotations available) but qualitatively shown to provide accurate semantic labeling for images via heatmaps of the scaled log-likelihood overlaid on the images. When sorted based on the median values per image, clear trends in quality are observed. Furthermore, downstream classification is shown to be possible and effective via a weakly supervised approach using the log-likelihood output from a normalizing flow model as a training signal for a feature-extracting convolutional neural network. The pre-linear dense layer outputs on the CNN are shown to disentangle high level representations and efficiently cluster various quality issues. Thus, an entirely non-annotated (fully unsupervised) approach is shown possible for accurate estimation and classification of quality issues..
Sun Yat-sen University（中山大学）；Guangdong Key Laboratory of Big Data Analysis and Processing（广东省大数据分析与处理重点实验室）
Generative adversarial networks (GANs) are known for their strong abilities on capturing the underlying distribution of training instances. Since the seminal work of GAN, many variants of GAN have been proposed. However, existing GANs are almost established on the assumption that the training dataset is clean. But in many real-world applications, this may not hold, that is, the training dataset may be contaminated by a proportion of undesired instances. When training on such datasets, existing GANs will learn a mixture distribution of desired and contaminated instances, rather than the desired…
This paper studies the distribution estimation of contaminated data by the MoM-GAN method, which combines generative adversarial net (GAN) and median-of-mean (MoM) estimation. We use a deep neural network (DNN) with a ReLU activation function to model the generator and discriminator of the GAN. Theoretically, we derive a non-asymptotic error bound for the DNN-based MoM-GAN estimator measured by integral probability metrics with the $b$-smoothness Hölder class. The error bound decreases essentially as $n^{-b/p}\vee n^{-1/2}$, where $n$ and $p$ are the sample size and the dimension of input data…
Generative modeling of granular flow on inclined planes using conditional flow matching
School of Construction, University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校建设学院）；Department of Civil, Environmental, and Construction Engineering, Texas Tech University（德克萨斯理工大学土木、环境与建设工程系）；College of Civil Engineering, Zhejiang University of Technology（浙江工业大学土木工程学院）
Harbin Institute of Technology（哈尔滨工业大学）；Peking University（北京大学）
Seismic full waveform inversion (FWI) is a powerful geophysical imaging technique that produces high-resolution subsurface models by iteratively minimizing the misfit between the simulated and observed seismograms. Unfortunately, conventional FWI with least-squares function suffers from many drawbacks such as the local-minima problem and computation of explicit gradient. It is particularly challenging with the contaminated measurements or poor starting models. Recent works relying on partial differential equations and neural networks show promising performance for two-dimensional FWI. Inspired…
We investigate the effectiveness of generative adversarial networks (GANs) for speech enhancement, in the context of improving noise robustness of automatic speech recognition (ASR) systems. Prior work demonstrates that GANs can effectively suppress additive noise in raw waveform speech signals, improving perceptual quality metrics; however this technique was not justified in the context of ASR. In this work, we conduct a detailed study to measure the effectiveness of GANs in enhancing speech contaminated by both additive and reverberant noise. Motivated by recent advances in image processing,…
University of California Santa Barbara（加州大学圣巴巴拉分校）；Washington University in St. Louis（圣路易斯华盛顿大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Cancer Center at Illinois（伊利诺伊癌症中心）
Purpose: To objectively assess new medical imaging technologies via computer-simulations, it is important to account for the variability in the ensemble of objects to be imaged. This source of variability can be described by stochastic object models (SOMs). It is generally desirable to establish SOMs from experimental imaging measurements acquired by use of a well-characterized imaging system, but this task has remained challenging. Approach: A generative adversarial network (GAN)-based method that employs AmbientGANs with modern progressive or multiresolution training approaches is proposed. …
Fast 3D Diffusion for Scalable Granular Media Synthesis
Aix Marseille Univ, CNRS, Centrale Med, LMA（艾克斯-马赛大学，法国国家科学研究中心，中央医疗学院，马赛实验室）；Université Paris Saclay, CentraleSupélec, ENS Paris-Saclay, CNRS, Laboratoire de Mécanique Paris-Saclay UMR 9026（巴黎萨克雷大学，中央超算学院，巴黎-萨克雷高等师范学院，法国国家科学研究中心，巴黎机械实验室（UMR 9026））；Innovation and Research Department, SNCF（法国国家铁路公司创新与研究部）
Identifying novel functional protein structures is at the heart of molecular engineering and molecular biology, requiring an often computationally exhaustive search. We introduce the use of a Deep Convolutional Generative Adversarial Network (DCGAN) to classify protein structures based on their functionality by encoding each sample in a grid object structure using three features in each object: the generic atom type, the position atom type, and its occupancy relative to a given atom. We train DCGAN on 3-dimensional (3D) decoy and native protein structures in order to generate and discriminate …
North Carolina State University（北卡罗来纳州立大学）；Intuit Inc.（财捷公司）
Robust anomaly detection is a requirement for monitoring complex modern systems with applications such as cyber-security, fraud prevention, and maintenance. These systems generate multiple correlated time series that are highly seasonal and noisy. This paper presents a novel unsupervised deep learning architecture for multivariate time series anomaly detection, called Robust Seasonal Multivariate Generative Adversarial Network (RSM-GAN). It extends recent advancements in GANs with adoption of convolutional-LSTM layers and an attention mechanism to produce state-of-the-art performance. We condu…
Large-scale mobile traffic analytics is becoming essential to digital infrastructure provisioning, public transportation, events planning, and other domains. Monitoring city-wide mobile traffic is however a complex and costly process that relies on dedicated probes. Some of these probes have limited precision or coverage, others gather tens of gigabytes of logs daily, which independently offer limited insights. Extracting fine-grained patterns involves expensive spatial aggregation of measurements, storage, and post-processing. In this paper, we propose a mobile traffic super-resolution techni…
Obtaining reliable data describing local poverty metrics at a granularity that is informative to policy-makers requires expensive and logistically difficult surveys, particularly in the developing world. Not surprisingly, the poverty stricken regions are also the ones which have a high probability of being a war zone, have poor infrastructure and sometimes have governments that do not cooperate with internationally funded development efforts. We train a CNN on free and publicly available daytime satellite images of the African continent from Landsat 7 to build a model for predicting local econ…
In this paper, we propose the coarse-to-fine optimization for the task of speech enhancement. Cosine similarity loss [1] has proven to be an effective metric to measure similarity of speech signals. However, due to the large variance of the enhanced speech with even the same cosine similarity loss in high dimensional space, a deep neural network learnt with this loss might not be able to predict enhanced speech with good quality. Our coarse-to-fine strategy optimizes the cosine similarity loss for different granularities so that more constraints are added to the prediction from high dimension …
Vidyasirimedhi Institute of Science and Technology (VISTEC)（维迪亚西利梅迪科学技术学院）；King Mongkut’s University of Technology Thonburi (KMUTT)（国王科技大学吞武里分校）；Imperial College London（帝国理工学院）
Visual data can be understood at different levels of granularity, where global features correspond to semantic-level information and local features correspond to texture patterns. In this work, we propose a framework, called SPLIT, which allows us to disentangle local and global information into two separate sets of latent variables within the variational autoencoder (VAE) framework. Our framework adds generative assumption to the VAE by requiring a subset of the latent variables to generate an auxiliary set of observable data. This additional generative assumption primes the latent variables …
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Bosch Center for Artificial Intelligence（博世人工智能中心）；CISPA Helmholtz Center for Information Security（亥姆霍兹信息安全中心）
Prediction of trajectories such as that of pedestrians is crucial to the performance of autonomous agents. While previous works have leveraged conditional generative models like GANs and VAEs for learning the likely future trajectories, accurately modeling the dependency structure of these multimodal distributions, particularly over long time horizons remains challenging. Normalizing flow based generative models can model complex distributions admitting exact inference. These include variants with split coupling invertible transformations that are easier to parallelize compared to their autore…
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Shanghai Jiao Tong University（上海交通大学）
There are two types of deep generative models: explicit and implicit. The former defines an explicit density form that allows likelihood inference; while the latter targets a flexible transformation from random noise to generated samples. While the two classes of generative models have shown great power in many applications, both of them, when used alone, suffer from respective limitations and drawbacks. To take full advantages of both models and enable mutual compensation, we propose a novel joint training framework that bridges an explicit (unnormalized) density estimator and an implicit sam…
Generative Molecular Design with Steerable and Granular Synthesizability Control
Laboratory of Artificial Chemical Intelligence (LIAC)（人工化学智能实验室）；NCCR Catalysis（催化联合研究所）；Laboratory of Sustainable and Catalytic Processing (LPDC)（可持续与催化加工实验室）；CHEMSPACE LLC；Enamine Ltd.（Enamine有限公司）；Taras Shevchenko National University of Kyiv（基塔斯·谢甫琴科基辅国立大学）；V. P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry（V. P. Kukhar生物有机化学与石油化学研究所）；Palladin Institute of Biochemistry（Palladin生物化学研究所）；Laboratory of Catalysis and Organic Synthesis (LCSO)（催化与有机合成实验室）；Laboratory of Protein Design and Immunoengineering (LPDI)（蛋白质设计与免疫工程实验室）
Generative AI for End-to-End Limit Order Book Modelling: A Token-Level Autoregressive Generative Model of Message Flow Using a Deep State Space Network
University of Oxford（牛津大学）；Oxford-Man Institute of Quantitative Finance（牛津-曼量化金融研究院）；Alan Turing Institute（艾伦·图灵研究所）；Man Group（英仕曼集团）；Foerster Lab for AI Research（福斯特人工智能研究实验室）
Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；National Energy Research Scientific Computing Center（国家能源研究科学计算中心）；Berkeley Institute for Data Science（伯克利数据科学研究所）；University of California, Berkeley（加州大学伯克利分校）；University of California, Riverside（加州大学河滨分校）；Thomas Jefferson National Accelerator Facility（托马斯·杰斐逊国家加速器设施）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）
