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Dual Sequential variational autoencoders for Fraud Detection 1

18th International Symposium on Intelligent Data Analysis (IDA)

作者： Alazizi, Ayman Habrard, Amaury Jacquenet, Francois He-Guelton, Liyun Oble, Frederic Univ Lyon Lab Hubert Curien Univ St Etienne UMR CNRS 5516 F-42000 St Etienne France Worldline F-95870 Bezons France

ISBN: (数字)9783030445843

ISBN: (纸本)9783030445843;9783030445836

Fraud detection is an important research area where machine learning has a significant role to play. An important task in that context, on which the quality of the results obtained depends, is feature engineering. Unfortunately, this is very time and human consuming. Thus, in this article, we present the DuSVAE model that consists of a generative model that takes into account the sequential nature of the data. It combines two variational autoencoders that can generate a condensed representation of the input sequential data that can then be processed by a classifier to label each new sequence as fraudulent or genuine. The experiments we carried out on a large real-word dataset, from the Worldline company, demonstrate the ability of our system to better detect frauds in credit card transactions without any feature engineering effort.

关键词： Anomaly detection Fraud detection Sequential data variational autoencoder

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3D hand pose estimation from a single RGB image through semantic decomposition of VAE latent space

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PATTERN ANALYSIS AND APPLICATIONS 2022年第1期25卷 157-167页

作者： Guo, Xinru Xu, Song Lin, Xiangbo Sun, Yi Ma, Xiaohong Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China

Based on the disentanglement representation learning theory and the cross-modal variational autoencoder (VAE) model, we derive a "Single Input Multiple Output" (SIMO) disentangled model cmSIMO - beta VAE. With the guidance of this derived model, we design a new VAE network, named da-VAE, for the challenging task of 3D hand pose estimation from a single RGB image. The designed da-VAE network has a multi- head encoder with the attention modules. Cooperating with the specific supervisions, the latent space is decomposed into subspaces with explicit semantics, which are relevant to the generative factors of hand pose, shape, appearance and others. The performance of the proposed da-VAE network is evaluated on RHD and STB dataset. The experimental results show competitive accuracies with the state-of-the-art methods.

关键词： Hand pose estimation Monocular RGB image Disentanglement representation learning variational autoencoder

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Developing Soft-Sensor Models Using Latent Dynamic variational autoencoders

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IFAC-PapersOnLine 2021年第3期54卷 61-66页

作者： Yi Shan Lee Sai Kit Ooi Dave Tanny Junghui Chen Department of Chemical Engineering Chung-Yuan Christian University Tao-Yuan Taiwan R.O.C.

Quality variables, which are usually measured offline, play important roles in describing process behaviors. However, online data obtained from soft sensors are significant as they provide accurate and immediate information. The reliability of online soft sensors is questionable due to changes in sensors, equipment, raw material availability, and operation conditions. In addition, chemical plants have dynamic properties and complex correlations amidst a large number of process variables. This causes most of the predictions obtained from steady-state soft sensors to be inaccurate in representing the particular chemical process. In this paper, the latent dynamic variational autoencoder is proposed to provide an estimation model and supervise soft-sensors. The input data are encoded in the latent space to remove underlying noises and disturbances in the data. Afterward, the dynamical properties are learned in the latent space through the bi-directional recurrent neural network, whose output (latent variable) is used to reconstruct back the input data. A simulation case study is conducted to show the effectiveness of the proposed method.

关键词： Dynamic Nonlinear Process Soft-Sensor Prediction Supervised variational autoencoder

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TOWARDS EFFICIENT variational AUTO-ENCODER USING WASSERSTEIN DISTANCE 29

TOWARDS EFFICIENT VARIATIONAL AUTO-ENCODER USING WASSERSTEIN...

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IEEE International Conference on Image Processing (ICIP)

作者： Chen, Zichuan Liu, Peng Washington Univ St Louis St Louis MO 14263 USA Singapore Management Univ Singapore Singapore

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

VAE, or variational auto-encoder, compresses data into latent attributes and generates new data of different varieties. VAE with KL divergence loss has been considered an effective technique for data augmentation. In this paper, we propose using Wasserstein distance as a measure of distributional similarity for the latent attributes and show its superior theoretical lower bound (ELBOW) compared with that of KL divergence (ELBOKL) under mild conditions. Using multiple experiments, we demonstrate that the new loss function converges faster and generates better quality data to aid image classification tasks. We also propose implementing a dynamically changing hyper-parameter tuning schedule to avoid the potential overfitting of ELBOW.

关键词： Image reconstruction variational autoencoder generative models Wasserstein distance

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IMPROVING EMOTIONAL SPEECH SYNTHESIS BY USING SUS-CONSTRAINED VAE AND TEXT ENCODER AGGREGATION 47

IMPROVING EMOTIONAL SPEECH SYNTHESIS BY USING SUS-CONSTRAINE...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Yang, Fengyu Luan, Jian Wang, Yujun Xiaomi Corp Beijing Peoples R China

ISBN: (纸本)9781665405409

Learning emotion embedding from reference audio is a straightforward approach for multi-emotion speech synthesis in encoder-decoder systems. But how to get better emotion embedding and how to inject it into TTS acoustic model more effectively are still under investigation. In this paper, we propose an innovative constraint to help VAE extract emotion embedding with better cluster cohesion. Besides, the obtained emotion embedding is used as query to aggregate latent representations of all encoder layers via attention. Moreover, the queries from encoder layers themselves are also helpful. Experiments prove the proposed methods can enhance the encoding of comprehensive syntactic and semantic information and produce more expressive emotional speech.

关键词： emotional TTS variational autoencoder emotion embedding encoder aggregation

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XCSR with VAE using Gaussian Distribution Matching: From Point to Area Matching in Latent Space for Less-overlapped Rule Generation in Observation Space

XCSR with VAE using Gaussian Distribution Matching: From Poi...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Yatsu, Naoya Shiraishi, Hiroki Sato, Hiroyuki Takadama, Keiki Univ Electrocommun 1-5-1 Chofugaoka Chofu Tokyo 1828585 Japan

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

This paper focuses on the matching mechanism of Learning Classifier System (LCS) in a continuous space and proposes a novel matching mechanism based on Gaussian distribution. This mechanism can match the "area" instead of the "point (one value)" in the continuous space unlike the conventional LCS such as XCSR (XCS with Continuous-Valued Inputs). Such an area matching contributes to generating the rules (called classifiers) with less-overlapped with other rules. Concretely, the proposed area matching mechanism employed in XCSR using VAE can generate appropriate classifiers for latent variables with high-dimensional inputs by VAE and create a human-interpretable observation space of human-interpretable classifiers. Since the latent variable in VAE is followed by Gaussian distribution, the following three matching mechanisms are compared: (i) the (single) point matching that selects the classifier which condition covers the mean of Gaussian distribution mu;(ii) the multiple points matching that selects the classifier which condition covers the data sampled from Gaussian distribution (mu, sigma);and (iii) the area matching that selects the classifier which condition roughly covers a certain area of Gaussian distribution (mu, sigma). Through the intensive experiments on the high dimension maze problem, the following implications have been revealed: (1) the point matching in XCSR with VAE generates the ambiguous classifiers which conditions are overlapped with the other classifiers with the different action;(2) the sampling multiple points matching in XCSR with VAE has a potential of generating the less-overlapped classifiers by improving the data set through sampling. (3) the proposed area matching can generate the less-overlapped classifiers with the same learning steps, which corresponds to the time of the point matching.

关键词： XCS variational autoencoder data mining

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Interpreting variational autoencoders with Fuzzy Logic: A step towards interpretable deep learning based fuzzy classifiers

Interpreting Variational Autoencoders with Fuzzy Logic: A st...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Bolat, Kutay Kumbasar, Tufan Istanbul Tech Univ Dept Control & Automat Engn Istanbul Turkey

ISBN: (纸本)9781728169323

The emerging success of Deep Learning (DL) in various application areas comes also with the questions starting with "How"s and "Why"s. These questions can be answered if the DL methods are interpretable and thus provide a certain a degree of explanation. In this paper, we propose a DL framework that leverages the advantages of beta-variational autoencoder (VAE) and Fuzzy Sets (FSs), which are disentanglement and linguistic representation, for the design of a novel DL based Fuzzy Classifier (FC). We first present a step-by-step design approach to construct the DL-FC which is composed of the encoder layer of beta-VAE and a Fuzzy Logic System (FLS) followed by a softmax layer. The beta-VAE is trained so that the semantic information of the high dimensional data is captured. The latent space of the beta-VAE is clustered to extract FSs. The FSs are then used to define antecedents of the FLS that is trained with DL methods. We present results conducted on the MNIST dataset and showed that DL-FC is quite competitive with its deep neural network counterpart. We then try to provide an interpretation to the antecedents of FLS by examining the FSs, the latent traversals and heat-maps of each latent dimension. The results show that the antecedents of FLS can be defined with linguistic interpretations. Thus, for the first time in the literature, we showed that linguistic interpretations can be defined for the latent space of beta-VAE with FSs.

关键词： variational autoencoder fuzzy sets fuzzy c-means clustering classification interpretation

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Sequential Segment-based Level Generation and Blending using variational autoencoders 20

Sequential Segment-based Level Generation and Blending using...

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15th International Conference on the Foundations of Digital Games

作者： Sarkar, Anurag Cooper, Seth Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9781450388078

Existing methods of level generation using latent variable models such as VAEs and GANs do so in segments and produce the final level by stitching these separately generated segments together. In this paper, we build on these methods by training VAEs to learn a sequential model of segment generation such that generated segments logically follow from prior segments. By further combining the VAE with a classifier that determines whether to place the generated segment to the top, bottom, left or right of the previous segment, we obtain a pipeline that enables the generation of arbitrarily long levels that progress in any of these four directions and are composed of segments that logically follow one another. In addition to generating more coherent levels of non-fixed length, this method also enables implicit blending of levels from separate games that do not have similar orientation. We demonstrate our approach using levels from Super Mario Bros., Kid Icarus and Mega Man, showing that our method produces levels that are more coherent than previous latent variable-based approaches and are capable of blending levels across games.

关键词： procedural content generation variational autoencoder level generation level blending game blending PCGML

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CONTINUAL LEARNING OF PREDICTIVE MODELS IN VIDEO SEQUENCES VIA variational autoencoderS

CONTINUAL LEARNING OF PREDICTIVE MODELS IN VIDEO SEQUENCES V...

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IEEE International Conference on Image Processing (ICIP)

作者： Campo, Damian Slavic, Giulia Baydoun, Mohamad Marcenaro, Lucio Regazzoni, Carlo Univ Genoa DITEN Genoa Italy

ISBN: (纸本)9781728163956

This paper proposes a method for performing continual learning of predictive models that facilitate the inference of future frames in video sequences. For a first given experience, an initial variational autoencoder, together with a set of fully connected neural networks are utilized to respectively learn the appearance of video frames and their dynamics at the latent space level. By employing an adapted Markov Jump Particle Filter, the proposed method recognizes new situations and integrates them as predictive models avoiding catastrophic forgetting of previously learned tasks. For evaluating the proposed method, this article uses video sequences from a vehicle that performs different tasks in a controlled environment.

关键词： Continual learning lifelong learning variational autoencoder particle filter kalman filter

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CO2 plume Imaging with accelerated deep learning-based data Assimilation: Applications at the Illinois Basin-Decatur Carbon sequestration project

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FUEL 2025年 385卷

作者： Sakai, Takuto Nagao, Masahiro Datta-Gupta, Akhil Mishra, Srikanta Texas A&M Univ 3116 TAMU College Stn TX 77843 USA

It is crucial to monitor the CO2 plume effectively throughout the life cycle of a geologic CO2 sequestration project to ensure safety and storage efficiency. However, the computational cost of existing data assimilation methods can be prohibitively expensive due to the complex physics with multi-component non-isothermal simulation and high dimensionality of field-scale reservoir models. We address this challenge by proposing an accelerated deep learning-based workflow for model calibration and prediction of CO2 plume evolution in the reservoir. In the proposed workflow, a neural network model utilizes available monitoring data such as downhole pressure and temperature measurements as input and predicts the propagating pressure 'front' using the diffusive time of flight (DTOF) map which is considered as representative reservoir image of the flow field. The DTOF is the arrival time of pressure front propagation, which can be computed by the Fast Marching Method (FMM) rapidly without flow simulations. Reservoir model calibration can be implemented by selecting the training data samples that describe the predicted DTOF map based on observed data. The computational efficiency of the data assimilation framework is significantly enhanced in two ways. First, instead of using multiple CO2 saturation maps for different timesteps, a single DTOF map is used as the output image. Since the reservoir dynamics is compressed into a single DTOF image, the memory and computational cost are reduced significantly. Second, an optimum coarsening of geologic model is applied, which substantially reduces the training data generation cost. The optimum coarsening scheme is utilized to maximize the computational efficiency while minimizing the error of simulated monitoring data, such as well pressure and temperature data. The power and efficacy of our workflow is demonstrated by application to the Illinois Basin-Decatur Project (IBDP), a large-scale CO2 storage test in saline aquifer. We achie

关键词： Deep learning Data Assimilation variational autoencoder Diffusive time of flight CO2 Storage

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