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Unsupervised pathology detection in medical images using conditional variational autoencoders

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2019年第3期14卷 451-461页

作者： Uzunova, Hristina Schultz, Sandra Handels, Heinz Ehrhardt, Jan Univ Lubeck Inst Med Informat Lubeck Germany

PurposePathology detection in medical image data is an important but a rather complicated task. In particular, the big variability of the pathologies is a challenge to automatic detection methods and even to machine learning methods. Supervised algorithms would usually learn the appearance of a single pathological structure based on a large annotated dataset. As such data is not usually available, especially in large amounts, in this work we pursue a different unsupervised *** method is based on learning the entire variability of healthy data and detect pathologies by their differences to the learned norm. For this purpose, we use conditional variational autoencoders which learn the reconstruction and encoding distribution of healthy images and also have the ability to integrate certain prior knowledge about the data (condition).ResultsOur experiments on different 2D and 3D datasets show that the approach is suitable for the detection of pathologies and deliver reasonable Dice coefficients and AUCs. Also this method can estimate missing correspondences in pathological images and thus can be used as a pre-step to a registration method. Our experiments show improving registration results on pathological data when using this *** the presented approach is suitable for a rough pathology detection in medical images and can be successfully used as a preprocessing step to other image processing methods.

关键词： conditional variational autoencoder Unsupervised pathology detection Image registration

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Generating Robotic Emotional Body Language of Targeted Valence and Arousal with conditional variational autoencoders 15

Generating Robotic Emotional Body Language of Targeted Valen...

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15th Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Marmpena, Mina Garcia, Fernando Lim, Angelica Univ Plymouth Plymouth Devon England ANYbotics AG Zurich Switzerland Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781450370578

Non-verbal communication that encompasses emotional body language is a crucial aspect of social robotics applications. Deep learning models for the generation of robotic expressions of bodily affect gain more and more ground recently over the hand-coded methods. In this work, we present a conditional variational autoencoder network that generates emotional body language animations of targeted valence and arousal for a Pepper robot, and we conduct a user study to evaluate the interpretability of the generated animations.

关键词： human-robot interaction robotic animation emotional body language deep learning conditional variational autoencoder

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BOOSTING SPEECH ENHANCEMENT WITH CLEAN SELF-SUPERVISED FEATURES VIA conditional variational autoencoderS 49

BOOSTING SPEECH ENHANCEMENT WITH CLEAN SELF-SUPERVISED FEATU...

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

作者： Lee, Yoonhyung Jung, Kyomin Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea Seoul Natl Univ Automat & Syst Res Inst Seoul South Korea

ISBN: (纸本)9798350344868;9798350344851

Recently, Self-Supervised Features (SSF) trained on extensive speech datasets have shown significant performance gains across various speech processing tasks. Nevertheless, their effectiveness in Speech Enhancement (SE) systems is often suboptimal due to insufficient optimization for noisy environments. To address this issue, we present a novel methodology that directly utilizes SSFs extracted from clean speech for enhancing SE models. Specifically, we leverage the clean SSFs for latent space modeling within the conditional variational autoencoder (CVAE) framework. Consequently, we enable our model to fully leverage the knowledge existing in the clean SSFs without the interference of noise. In experiments, our approach yields clear improvements over existing methods that use SSFs across six evaluation metrics. Furthermore, we provide comprehensive analyses to validate the effectiveness of 1) incorporating clean SSFs within the CVAE framework and 2) the training techniques used to achieve optimal performance from our approach in SE systems. Code and audio samples are available at https://***/YoonhyungLee94/SSFCVAE

关键词： Speech enhancement self-supervised features conditional variational autoencoder posterior collapse

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Targeted Adversarial Examples Generating Method Based on cVAE in Black Box Settings

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Chinese Journal of Electronics 2021年第5期30卷 866-875页

作者： YU Tingyue WANG Shen ZHANG Chunrui WANG Zhenbang LI Yetian YU Xiangzhan School of Cyberspace Science Harbin Institute of Technology Institute of Computer Application China Academy of Engineering Physics State Grid Heilongjiang Power Co. Ltd.

In recent years, adversarial examples has become one of the most important security threats in deep learning applications. For testing the security of deep learning models in adversarial environment,many researches focus on generating adversarial examples quickly and efficiently. In order to solve the problems of existing generative adversarial networks based methods which can not effectively generate the targeted adversarial examples in black box settings, and to improve the temporal performance of gradient-based generating methods,an adversarial examples generating method based on conditional variational autoencoder(cVAE) is proposed in this paper, where a cVAE is designed elaborately to generate adversarial examples without most of the detailed information about the attacked deep learning models, of which the output can be controlled arbitrarily by these crafted inputs, used to test the robustness of deep learning models against adversarial examples. The experimental results show that the proposed method can achieve a comparable attack success rate and a better temporal performance than the existing gradient-based generating methods in black box environment.

关键词： adversarial environment targeted adversarial examples generating method black box settings conditional variational autoencoder deep learning (artificial intelligence) security of data gradient-based generating methods deep learning applications cVAE generative adversarial networks

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Improving Multi-Agent Trajectory Prediction Using Traffic States on Interactive Driving Scenarios

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第5期8卷 2708-2715页

作者： Vishnu, Chalavadi Abhinav, Vineel Roy, Debaditya Mohan, C. Krishna Babu, Ch. Sobhan Indian Inst Technol Hyderabad Dept Comp Sci & Engn Hyderabad 502285 Telangana India Inst High Performance Comp IHPC Agcy Sci Technol & Res ASTAR Singapore City 138632 Singapore

Predicting trajectories of multiple agents in interactive driving scenarios such as intersections, and roundabouts are challenging due to the high density of agents, varying speeds, and environmental obstacles. Existing approaches use relative distance and semantic maps of intersections to improve trajectory prediction. However, drivers base their driving decision on the overall traffic state of the intersection and the surrounding vehicles. So, we propose to use traffic states that denote changing spatio-temporal interaction between neighboring vehicles, to improve trajectory prediction. An example of a traffic state is a clump state which denotes that the vehicles are moving close to each other, i.e., congestion is forming. We develop three prediction models with different architectures, namely, Transformer-based (TS-Transformer), Generative Adversarial Network-based (TS-GAN), and conditional variational autoencoder-based (TS-CVAE). We show that traffic state-based models consistently predict better future trajectories than the vanilla models. TS-Transformer produces state-of-the-art results on two challenging interactive trajectory prediction datasets, namely, Eye-on-Traffic (EOT), and INTERACTION. Our qualitative analysis shows that traffic state-based models have better aligned trajectories to the ground truth.

关键词： Trajectory Predictive models History Hidden Markov models Transformers Roads Decoding Trajectory prediction generative adversarial networks conditional variational autoencoder transformers

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Intelligent Condition-Based Monitoring of Rotary Machines With Few Samples

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IEEE SENSORS JOURNAL 2020年第23期20卷 14337-14346页

作者： Dixit, Sonal Verma, Nishchal K. Indian Inst Technol Kanpur Dept Elect Engn Kanpur 208016 Uttar Pradesh India

Recently, intelligent condition based monitoring systems build on deep learning methods have gained popularity. The success of these methods relies upon the large labeled training datasets, which are crucial to collect in industries. Therefore, building an effective fault diagnosis system becomes challenging. In this paper, a novel fault diagnosis framework is proposed to tackle the issue of limited samples in the training dataset. In the proposed framework, firstly, new training samples termed as synthetic samples are generated to increase the size of the dataset. After that, both original and synthetic samples are stacked, and the classifier model is trained. This study proposes a modified conditional variational autoencoder (CVAE) to generate synthetic samples. In the proposed CVAE, centroid loss is added to the standard CVAE objective function. This loss directs generated samples to remain close with the centroid of their respective class, which helps in generating synthetic samples quite similar to the original samples. This paper also investigates the performance of proposed model in the presence of noise and effect of transformed data and original data. To verify the effectiveness of the proposed approach, the Air compressor and Case Western Reserve University datasets have been investigated. For the CWRU dataset with only 80 samples, accuracy of 96.39%, 99.58%, 98.33% was obtained using multilayer neural network, support vector machine, and RF classifiers respectively. Classification accuracy increased to 63.33% when modified CVAE is used instead of standard CVAE. Finally, a comparative analysis between proposed methods and other state-of-the-art methods has been presented.

关键词： Training Fault diagnosis Intelligent sensors Microsoft Windows Monitoring Data models Sensor data conditional variational autoencoder condition based monitoring synthetic data

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Parametrische Modellierung und generatives tiefes Lernen für den Brückenentwurf

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BAUTECHNIK 2024年第3期101卷 174-180页

作者： Kraus, Michael A. Kuhn, Sophia V. Hodel, Anna Bischof, Rafael Maissen, Alessandro Salamanca Mino, Luis Perez-Cruz, Fernando Swiss Fed Inst Technol Inst Baustat & Konstrukt Prof Massiv & Bruckenbau Stefano Franscini Platz 5 CH-8093 Zurich Switzerland Andreasturm 18th FloorAndreasstr 5 CH-8050 Zurich Switzerland

In Anbetracht der erheblichen Umweltauswirkungen des Bauwesens wird die Analyse und v. a. Optimierung der Nachhaltigkeit von Strukturen unter Beibehaltung des etablierten Zuverlassigkeitsniveaus immer wichtiger. Im Hochbausektor existieren erste Werkzeuge zur Lebenszyklusanalyse, diese sind jedoch nicht direkt ubertragbar auf Bruckentragwerke. Dieser Beitrag fasst die wesentlichen Ansatze und Ergebnisse von aktuellen Forschungsprojekten der Autoren an der ETH zusammen und erlautert insbesondere einen neuen Deep-Learning-basierten Ansatz zur Erkundung und Modellierung des Entwurfsraums parametrischer Bruckenmodelle und deren Leistungsbewertungen und veranschaulicht die Anwendung fur eine Mehrzieloptimierung von Stahlbetonrahmenbrucken. Zunachst werden Daten unter Verwendung eines parametrischen Bruckenmodells sowie der Ankoppelung von Analysesoftware synthetisch generiert und anschliessend bedingte variationelle autoencoder (CVAE) als Metamodell trainiert. Der CVAE dient im Rahmen des konzeptionellen Bruckenentwurfs als effizienter Co-Pilot sowohl fur die Vorwarts- als auch Ruckwartsanalyse. Die mit dem CVAE durchgefuhrte Sensitivitatsanalyse zeigt Beziehungen zwischen Entwurfsparametern und/oder Leistungskenngrossen sowie Optimierungspotenziale auf. Das hier vorgestellte integrierte Framework besitzt das Potenzial zur Realisierung einer effizienten Bruckenplanung unter insbesondere den Kriterien der Nachhaltigkeit und Tragsicherheit und kann problemlos auf andere parametrische Fragestellungen erweitert werden. Parametric modeling and generative deep learning for bridge designGiven the significant environmental impact of the construction industry, the analysis and, above all, optimization of the sustainability of structures while maintaining established levels of reliability are becoming increasingly important. While there are initial tools for life cycle analysis in the building sector, these are not directly transferable to bridge structures. This paper introduces

关键词： Bruckenbau kunstliche Intelligenz generativer Entwurf bedingte variationelle autoencoder bridge design artificial intelligence generative design conditional variational autoencoder

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Health Indicator for Low-Speed Axial Bearings Using variational autoencoders

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IEEE ACCESS 2020年 8卷 35842-35852页

作者： Hemmer, Martin Klausen, Andreas Huynh Van Khang Robbersmyr, Kjell G. Waag, Tor, I Univ Agder Dept Engn Sci N-4877 Grimstad Norway MHWirth AS N-4639 Kristiansand Norway NORCE Norwegian Res Ctr AS N-4877 Grimstad Norway

This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational inference using variational autoencoders (VAEs), trained on each speed reference in the dataset. Further, versatility is added by conditioning on the speed, extending the VAE to a conditional VAE (CVAE), thereby incorporating all speeds in a single model. Within the framework, the coefficients of autoregressive (AR) models are used as features. The dimensionality reduction inherent in the proposed method lowers the need of expert knowledge to design good condition indicators. Moreover, the suggested methodology allows for setting the probability of false alarms when encoding new data points to the latent variable space using the trained model. The effectiveness of the proposed method is validated based on two different datasets: from a workshop test of an offshore drilling machine and from an in-house test rig for axial bearings. In both datasets, the HI is exceeding the warning and alarm levels with a probability of false alarm (PFA) of 10(-6), and the method is most effective at lower shaft speeds.

关键词： Training Decoding Dimensionality reduction Drilling machines Fault detection Industries Rolling bearings Bearing fault detection condition monitoring unsupervised learning variational autoencoder conditional variational autoencoder generative models

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EmoMusicTV: Emotion-Conditioned Symbolic Music Generation With Hierarchical Transformer VAE

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 1076-1088页

作者： Ji, Shulei Yang, Xinyu Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China

Emotion is one of the most crucial attributes of music. However, due to the scarcity of emotional music datasets, emotion-conditioned symbolic music generation using deep learning techniques has not been investigated in depth. In particular, no study explores conditional music generation with the guidance of emotion, and few studies adopt time-varying emotional conditions. To address these issues, first, we endow three public lead sheet datasets with fine-grained emotions by automatically computing the valence labels from the chord progressions. Second, we propose a novel and effective encoder-decoder architecture named EmoMusicTV to explore the impact of emotional conditions on multiple music generation tasks and to capture the rich variability of musical sequences. EmoMusicTV is a transformer-based variational autoencoder (VAE) that contains a hierarchical latent variable structure to model holistic properties of the music segments and short-term variations within bars. The piece-level and bar-level emotional labels are embedded in their corresponding latent spaces to guide music generation. Third, we pretrain EmoMusicTV with the lead sheet continuation task to further improve its performance on conditional melody or harmony generation. Experimental results demonstrate that EmoMusicTV outperforms previous methods on three tasks, i.e., melody harmonization, melody generation given harmony, and lead sheet generation. Ablation studies verify the significant roles of emotional conditions and hierarchical latent variable structure on conditional music generation. Human listening shows that the lead sheets generated by EmoMusicTV are closer to the ground truth (GT) and perform slightly worse than the GT in conveying emotional polarity.

关键词： Symbolic music generation lead sheets emotional condition conditional variational autoencoder transformer

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Multi-Label Zero-Shot Learning With Adversarial and variational Techniques

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IEEE ACCESS 2024年 12卷 94990-95006页

作者： Gull, Muqaddas Arif, Omar Natl Univ Sci & Technol NUST Sch Elect Engn & Comp Sci Islamabad 44000 Pakistan Amer Univ Sharjah Dept Comp Sci & Engn Sharjah U Arab Emirates

Multi-label zero-shot learning expands upon the traditional single-label zero-shot learning paradigm by addressing the challenge of accurately classifying images containing multiple unseen classes, which are not part of the training data. Current techniques rely on attention mechanisms to tackle the complexities of multi-label zero-shot learning (ZSL) and generalized zero-shot learning (GZSL). However, the generation of features, especially within the context of a generative approach, remains an unexplored area. In this paper, we propose a generative approach that leverages the capabilities of conditional variational autoencoder (CVAE) and conditional Generative Adversarial Network (CGAN) to enhance the quality of generative data for both multi-label ZSL and GZSL. Additionally, we introduce a novel "Regressor" as a supplementary tool to improve the reconstruction of visual features. This Regressor operates in conjunction with a "cycle-consistency loss" to ensure that the generated features preserve the key qualities of the original features even after undergoing transformations. To gauge the efficacy of our proposed approach, we conducted comprehensive experiments on two widely recognized benchmark datasets: NUS-WIDE and MS COCO. Our evaluation spanned both multi-label ZSL and GZSL scenarios. Notably, our approach yielded significant enhancements in mean Average Precision (mAP) for both datasets. Specifically, we observed a 0.2% increase in performance on the NUS-WIDE dataset and a notable 2.6% improvement on the MS COCO dataset in the context of Multi-label ZSL. The results clearly demonstrate that our generative approach outperforms existing methods on these widely-recognized datasets.

关键词： Semantics Visualization Generative adversarial networks Task analysis Data models Zero-shot learning Training Encoders conditional variational autoencoder conditional generative adversarial network generalized zero-shot learning regressor zero-shot learning

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