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检索条件"主题词=variational autoencoder"

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Modeling variational Anchoring Effect for Recommender Systems 2

Modeling Variational Anchoring Effect for Recommender System...

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2nd IEEE Conference on Artificial Intelligence (CAI)

作者： Xiao, Yudi Zhang, Yingyi Li, Xianneng Dalian Univ Technol Sch Econ & Management Inst Adv Intelligence Dalian Peoples R China

ISBN: (纸本)9798350354102;9798350354096

Users generally have a tendency to rely on numerical information of recommendations presented on the web page when judging the recommended items, which refers to a classic psychological concept, anchoring effect. Learning users' psychology from explicit behaviors has been widely applied in RS and performs well on capturing user preferences and guiding the prediction tasks of recommendations. Recent studies have empirically proven that the anchoring effect can mislead users to click/purchase items that are not liked in principle, which will bring bias and noise to behavior data. However, vast majority of existing recommendation algorithms trained on behavior data ignore the anchoring bias, which results in suboptimal recommendations. In this paper, we propose a novel method named variational Anchoring Effect Encoder (VAEE) to model the anchoring effect and mitigate the anchoring bias for recommender systems. The proposed method mainly includes two steps: 1) User Anchoring Effect Module which aims to reconstruct the unanchored user preferences with a variational autoencoder (VAE)-based deep structure, and 2) User Anchoring Debias Module that generate the recommendation results with the reconstructed unbiased user representations. Extensive experiments on real-world datasets are conducted to demonstrate that reducing anchoring effect can bring particular improvement in AUC and the proposed VAEE is attachable to most existing recommendation models. We also compare the recommendation quality when using different anchoring feature subsets, which indicates that the learned representation of anchoring effect is authentic and truly effective to restore users' true preferences.

关键词： anchoring bias anchoring effect recommender systems variational autoencoder

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The Temporal Random Scene Generation Method Considering Source-Load Correlation 7

The Temporal Random Scene Generation Method Considering Sour...

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7th International Conference on Energy, Electrical and Power Engineering (CEEPE)

作者： Zhou, Hao Hong, Lucheng Liu, Hui Gu, Jie Zhu, Zihan Ren, Xiaofeng Southeast Univ Jiangsu Prov Key Lab Smart Grid Technol & Equipme Nanjing Peoples R China Jiangsu Elect Power Supply Co Xuzhou Elect Power Supply Branch Off Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9798350375794;9798350375800

With the continuous increase in the penetration rate of renewable distribution generation (RDG) and the integration of various flexible controllable loads in new distribution systems, the uncertainty from both generation and demand sides poses significant challenges to the planning and scheduling of active distribution networks (AND). In response to this issue, this paper proposes a novel distribution network joint scenario generation method incorporating source-load correlation variational autoencoder (VAE), accompanied by the design of appropriate encoder and decoder network architectures. This method intrinsically integrates a position encoding module into the conventional VAE structure, employs temporal convolutional networks (TCN) for extracting correlation features of multi-dimensional stochastic variables related to source and load. Subsequently, simulation analysis is conducted based on actual photovoltaic and load data. The results demonstrate the effectiveness of the proposed model and method in learning the stochasticity and correlation of multi-dimensional time series variables, thus providing a reliable scenario modeling approach for planning and long-term operation.

关键词： New Distribution System renewable distribution generation Scenario Generation variational autoencoder

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Unsupervised Anomaly Detection in 3D Brain FDG PET: A Benchmark of 17 VAE-Based Approaches 3rd

Unsupervised Anomaly Detection in 3D Brain FDG PET: A Benchm...

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3rd Workshop on Deep Generative Models for Medical Image Computing and Computer Assisted Intervention (DGM4MICCAI) at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

作者： Hassanaly, Ravi Brianceau, Camille Colliot, Olivier Burgos, Ninon Sorbonne Univ Hop Pitie Salpetriere AP HP Inst CerveauParis Brain InstICMCNRSInsermInr F-75013 Paris France

ISBN: (纸本)9783031537660;9783031537677

The use of deep generative models for unsupervised anomaly detection is an area of research that has gained interest in recent years in the field of medical imaging. Among all the existing models, the variational autoencoder (VAE) has proven to be efficient while remaining simple to use. Much research to improve the original method has been achieved in the computer vision literature, but rarely translated to medical imaging applications. To fill this gap, we propose a benchmark of fifteen variants of VAE that we compare with a vanilla autoencoder and VAE for a neuroimaging use case relying on a simulation-based evaluation framework. The use case is the detection of anomalies related to Alzheimer's disease and other dementias in 3D FDG PET. We showthat among the fifteen VAE variants tested, nine lead to a good reconstruction accuracy and are able to generate healthy-looking images. This indicates that many approaches developed for computer vision applications can generalize to the unsupervised detection of anomalies of various shapes, intensities and locations in 3DFDGPET. However, these models do not outperform the vanilla autoencoder and VAE.

关键词： variational autoencoder Deep generative models Unsupervised anomaly detection PET Alzheimer's disease

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Can I Trust My Anomaly Detection System? A Case Study Based on Explainable AI 2nd

Can I Trust My Anomaly Detection System? A Case Study Based ...

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2nd World Conference on Explainable Artificial Intelligence (xAI)

作者： Rashid, Muhammad Amparorel, Elvio Ferrari, Enrico Verda, Damiano Univ Torino Comp Sci Dept Cso Svizzera 185 I-10149 Turin Italy Rulex Innovat Labs Via Felice Romani 9 I-16122 Genoa Italy

ISBN: (纸本)9783031638022;9783031638039

Generative models based on variational autoencoders are a popular technique for detecting anomalies in images in a semi-supervised context. A common approach employs the anomaly score to detect the presence of anomalies, and it is known to reach high level of accuracy on benchmark datasets. However, since anomaly scores are computed from reconstruction disparities, they often obscure the detection of various spurious features, raising concerns regarding their actual efficacy. This case study explores the robustness of an anomaly detection system based on variational autoencoder generative models through the use of eXplainable AI methods. The goal is to get a different perspective on the real performances of anomaly detectors that use reconstruction differences. In our case study we discovered that, in many cases, samples are detected as anomalous for the wrong or misleading factors.

关键词： anomaly detection variational autoencoder eXplainable AI

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Compositionality and Generalization in Emergent Communication using Metropolis-Hastings Naming Game

Compositionality and Generalization in Emergent Communicatio...

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IEEE International Conference on Development and Learning (ICDL)

作者： Le Hoang, Nguyen Matsui, Yuta Hagiwara, Yoshinobu Ritsumeikan Univ Grad Sch Info Sci & Engn Osaka Japan Ritsumeikan Univ Res Org Sci & Tech Osaka Shiga Japan

ISBN: (纸本)9798350348569;9798350348552

This study investigates the emergence of compositionality and generalization within Emergent Communication (EmCom) systems, focusing on emergent language using the Metropolis-Hastings naming game (MHNG). Although the MHNG has been used in previous EmCom research, this is the first study to explore compositionality, the ability to construct complex expressions by combining simpler elements, and generalization, the ability to apply learned patterns to new situations, in emergent language within this language game. We introduce the novel Inter-VAE+VAE model, which equips each agent with dual variational autoencoders (VAEs), specifically designed for cognitive tasks that mirror human perception and language processing. This model, facilitating predictive coding, allows agents to refine their world models through collective experiences, a process rooted in the collective predictive coding hypothesis and differing from isolated learning approaches. Our model was evaluated against baseline models, including the beta-VAE and ss-TCVAE, and was further compared with implementations of the Lewis signaling game using the dSprites and 3Dshapes datasets. The results from these evaluations indicate an improvement in agent communication within the MH naming game and underscore the model's potential in replicating key aspects of human language, particularly compositionality and generalization, in artificial systems.

关键词： emergent communication symbol emergence metropolis-hastings naming game compositionality generalization variational autoencoder

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Action Conditioned Attention Encoder-Decoder and Discriminator for Human Motion Generation 1

引用

5th International Conference on Deep Learning Theory and Applications (DeLTA)

作者： Bandi, Chaitanya Thomas, Ulrike Tech Univ Chemnitz Chemnitz Germany

ISBN: (数字)9783031666940

ISBN: (纸本)9783031666933;9783031666940

We present a CVAE-GAN-based architecture for human motion generation with an action-conditioned variational autoencoder and a generative discriminator. In this work, we focus on generating more accurate actions performed by a single person using a conditioned generative model. The primary motivation for this work comes from human-robot collaboration scenarios where a person interacts with the robot using human actions. Our approach consists of a self-attention-based conditional variational autoencoder for reconstructions and a graph network-based discriminator for realistic human motion quality. We evaluate our network on three open-source datasets known as HumanAct12, NTU 120 RGB+D, and Actions in Supermarket Dataset. The extensive experiments show that the presented approach works for various human motions and input representations, such as the SMPL pose parameters, trajectory data, and skeleton joints. We achieve higher accuracy compared to the state-of-the-art methods on all three datasets.

关键词： Human motion generation Human actions variational autoencoder

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Comparison of deep generative models for real-time generation of synthesized defective wafer maps

Comparison of deep generative models for real-time generatio...

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Conference on Real-Time Image Processing and Deep Learning

作者： Alam, Lamia Kehtarnavaz, Nasser Univ Texas Dallas Dept Elect & Comp Engn Richardson TX 75080 USA

ISBN: (纸本)9781510673878;9781510673861

Modern wafer inspection systems in Integrated Circuit (IC) manufacturing utilize deep neural networks. The training of such networks requires the availability of a very large number of defective or faulty die patterns on a wafer called wafer maps. The number of defective wafer maps on a production line is often limited. In order to have a very large number of defective wafer maps for the training of deep neural networks, generative models can be utilized to generate realistic synthesized defective wafer maps. This paper compares the following three generative models that are commonly used for generating synthesized images: Generative Adversarial Network (GAN), variational Auto-Encoder (VAE), and CycleGAN which is a variant of GAN. The comparison is carried out based on the public domain wafer map dataset WM-811K. The quality aspect of the generated wafer map images is evaluated by computing the five metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), inception score (IS), Frechet inception distance (FID), and kernel inception distance (KID). Furthermore, the computational efficiency of these generative networks is examined in terms of their deployment in a real-time inspection system.

关键词： Synthesized Wafer Maps Generative Adversarial Network variational autoencoder CycleGAN Real-Time Image Synthesis

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Parameter and Topology Optimization Method for IPM Motors Using Multimodal Neural Network 21

Parameter and Topology Optimization Method for IPM Motors Us...

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IEEE 21st Biennial Conference on Electromagnetic Field Computation (IEEE CEFC)

作者： Iwata, Kazuhisa Sasaki, Hidenori Igarashi, Hajime Nakagawa, Daisuke Ueda, Tomoya Hosei Univ Grad Sch Sci & Engn Koganei Tokyo Japan Hosei Univ Dept Elect & Elect Engn Koganei Tokyo Japan Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido Japan Nidec Corp Ctr Res & Dev Kawasaki Kanagawa Japan

ISBN: (纸本)9798350348958;9798350348965

This study proposes a novel parameter and topology optimization method for an interior permanent magnet synchronous motor using a multimodal neural network. The optimized shape obtained by the proposed method improves the manufacturability compared to the conventional method. In addition, the computational time of the optimization using the proposed method is reduced by about 99.9% compared to the optimization using only the finite element method.

关键词： interior permanent magnet motor multimodal neural network topology optimization variational autoencoder

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Audio-Visual Cross-Modal Generation with Multimodal variational Generative Model

Audio-Visual Cross-Modal Generation with Multimodal Variatio...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Xu, Zhubin Wang, Tianlei Liu, Dekang Hu, Dinghan Zeng, Huangiang Cao, Jiuwen Machine Learning & I Hlth Int Cooperat Base Zheji Hangzhou Zhejiang Peoples R China Hangzhou Dianzi Univ Artificial Intelligence Inst Hangzhou Zhejiang Peoples R China Huagiao Univ Sch Engn Fujian Peoples R China Huagiao Univ Sch Informat Sci & Engn Fujian Peoples R China

ISBN: (纸本)9798350330991;9798350331004

Audio and Visual are two important visual modalities in video content understanding. However, the absence of one modality may be observed in practical applications due to the real environmental factors, which leads to the information loss. Therefore, audio and visual fusion is focused on using the shared and complementary information between modalities to recover the missing modalities from the available data modalities. In this paper, an Adversarial Hierarchical variational autoencoder (Adv-HVAE) model is proposed to solve this problem of modality data loss. A multimodal representation is first learned using a hierarchical variational autoencoder (VAE) model that enables the generation of missing modal data under any subset of available modalities. Also to obtain a more robust multimodal representation, a feature generation network is utilized to approximate the latent distribution of missing modalities. Finally, the adversarial training network is shown to be effective in improving the data quality generated through the Adv-HVAE framework. Experimental results demonstrate that Adv-HVAE achieves best generation results on two benchmark datasets, avMNIST and Sub-URMP.

关键词： Audio-Visual fusion Cross-modal generation variational autoencoder Adversarial learning

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JOINT DATA-DRIVEN ANALYSIS OF VISUAL-ODOMETRIC ANOMALY SIGNALS IN GENERATIVE AI-BASED AGENTS 49

JOINT DATA-DRIVEN ANALYSIS OF VISUAL-ODOMETRIC ANOMALY SIGNA...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Slavic, Giulia Bracco, Mattia Marcenaro, Lucio Gomez, David Martin Regazzoni, Carlo Zontone, Pamela Univ Genoa Genoa Italy Univ Carlos III Madrid Getafe Spain

ISBN: (纸本)9798350374520;9798350374513

This paper presents a data-driven model that, by exploring the correlation between the data coming from two sensors (GPS and camera), allows us to explain the odometry anomalies by analyzing video data. Our approach uses a Markov Jump Particle Filter (MJPF) to process the odometry data of a vehicle, extracting its features and identifying instant anomalies. Simultaneously, the object causing the anomaly is detected and tracked in the video data. After that, the correlation between the odometric trajectories and the object trajectories is determined in both normal and abnormal cases. Another correlation coefficient is then employed to calculate the distance between the computed correlations. The proposed method is evaluated using multi-modal data collected from a vehicle operating in a closed environment, where pedestrians represent anomalies. We show that our system is able to distinguish which video anomaly better explains the odometry anomaly.

关键词： variational autoencoder anomaly detection particle filtering Kalman filtering explainability

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