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检索条件"主题词=variational autoencoder"

共 1532 条记录，以下是621-630 订阅

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New Methods for Explainable variational autoencoders

New Methods for Explainable Variational Autoencoders

引用

Conference on Artificial Intelligence for Security and Defence Applications

作者： White, Riley Baracat-Donovan, Brian Helmsen, John McCullough, Thomas Noblis Inc 2022 Edmund Halley Dr Reston VA 20191 USA

ISBN: (纸本)9781510667136;9781510667143

A new deep learning algorithm for performing anomaly detection and multi-class classification with explainability using counterfactuals is described. The system is a variational autoencoder (VAE) with a modified loss function and new methods for counterfactual identification. An additional hinge-loss term is added to VAE training. This enables convenient synthetic data generation and allows straightforward construction of multi-class counterfactuals. Counterfactuals are synthetic data generated to explain system decisions by answering the question: "If this data was not anomalous or was in another class, what modifications would need to be made?" To determine counterfactuals, a path is determined through the embedding space via adversarial attack-like techniques to minimize reconstruction error, with the restriction of minimally altering the number of columns changed. Large changes are allowed, unlike adversarial attack approaches, so changes are isolated and easily visible. Anomaly detection is performed by modifying a result to lower its anomalousness. Classification changes are performed by modifying the data to another class. Multi-class classification is performed on the embedding space of the VAE via an attached linear support vector machine (SVM). By adding the hinge-loss term to the VAE embedding training as well as the SVM, the embedding is modified to prefer class separation without being informed of the specific class labels. This causes the classes in the embedding space to be separated by hyperplanes, making counterfactual generation convenient and SVM classification accurate. Accuracy is shown to be comparable to other deep learners. Approaches to accommodating the image and time-series data are discussed.

关键词： Deep Learning Anomaly Detection Explainability variational autoencoder

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Bidirectional variational autoencoders

Bidirectional Variational Autoencoders

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Kosko, Bart Adigun, Olaoluwa Univ Southern Calif Dept Elect & Comp Engn Los Angeles CA 90007 USA

ISBN: (纸本)9798350359329;9798350359312

We present the new bidirectional variational autoencoder (BVAE) network architecture. The BVAE uses a single neural network both to encode and decode instead of an encoder-decoder network pair. The network encodes in the forward direction and decodes in the backward direction through the same synaptic web. Simulations compared BVAEs and ordinary VAEs on the four image tasks of image reconstruction, classification, interpolation, and generation. The image datasets included MNIST handwritten digits, Fashion-MNIST, CIFAR-10, and CelebA-64 face images. The bidirectional structure of BVAEs cut the parameter count by almost 50% and still slightly outperformed the unidirectional VAEs.

关键词： variational autoencoder bidirectional backpropagation directional likelihoods ELBO evidence lower bound

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Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and variational autoencoders 10th

Assessing the Impact of Blood Pressure on Cardiac Function U...

引用

10th International Workshop on Statistical Atlases and Computational Modelling of the Heart (STACOM)

作者： Puyol-Anton, Esther Ruijsink, Bram Clough, James R. Oksuz, Ilkay Rueckert, Daniel Razavi, Reza King, Andrew P. Kings Coll London Sch Biomed Engn & Imaging Sci London England St Thomas Hosp NHS Fdn Trust London England Imperial Coll London Biomed Image Anal Grp London England

ISBN: (纸本)9783030390747;9783030390730

Maintaining good cardiac function for as long as possible is a major concern for healthcare systems worldwide and there is much interest in learning more about the impact of different risk factors on cardiac health. The aim of this study is to analyze the impact of systolic blood pressure (SBP) on cardiac function while preserving the interpretability of the model using known clinical biomarkers in a large cohort of the UK Biobank population. We propose a novel framework that combines deep learning based estimation of interpretable clinical biomarkers from cardiac cine MR data with a variational autoencoder (VAE). The VAE architecture integrates a regression loss in the latent space, which enables the progression of cardiac health with SBP to be learnt. Results on 3,600 subjects from the UK Biobank show that the proposed model allows us to gain important insight into the deterioration of cardiac function with increasing SBP, identify key interpretable factors involved in this process, and lastly exploit the model to understand patterns of positive and adverse adaptation of cardiac function.

关键词： Cardiac function variational autoencoder Cardiac risk factors

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Anomaly Detection in Industrial Software Systems Using variational autoencoders 7

Anomaly Detection in Industrial Software Systems <i>Using Va...

引用

7th International Conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Kumarage, Tharindu De Silva, Nadun Ranawaka, Malsha Kuruppu, Chamal Ranathunga, Surangika Univ Moratuwa Dept Comp Sci & Engn Katubedda Sri Lanka

ISBN: (纸本)9789897582769

Industrial software systems are known to be used for performing critical tasks in numerous fields. Faulty conditions in such systems can cause system outages that could lead to losses. In order to prevent potential system faults, it is important that anomalous conditions that lead to these faults are detected effectively. Nevertheless, the high complexity of the system components makes anomaly detection a high dimensional machine learning problem. This paper presents the application of a deep learning neural network known as variational autoencoder (VAE), as the solution to this problem. We show that, when used in an unsupervised manner, VAE outperforms the well-known clustering technique DBSCAN. Moreover, this paper shows that higher recall can be achieved using the semi-supervised one class learning of VAE, which uses only the normal data to train the model. Additionally, we show that one class learning of VAE outperforms semi-supervised one class SVM when training data consist of only a very small amount of anomalous samples. When a tree based ensemble technique is adopted for feature selection, the obtained results evidently demonstrate that the performance of the VAE is highly positively correlated with the selected feature set.

关键词： Anomaly Detection Industrial Software Systems variational autoencoder VAE DBSCAN

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Generalizing Across Domains in Diabetic Retinopathy via variational autoencoders 26th

Generalizing Across Domains in Diabetic Retinopathy via Vari...

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26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Chokuwa, Sharon Khan, Muhammad H. Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031474002;9783031474019

Domain generalization for Diabetic Retinopathy (DR) classification allows a model to adeptly classify retinal images from previously unseen domains with various imaging conditions and patient demographics, thereby enhancing its applicability in a wide range of clinical environments. In this study, we explore the inherent capacity of variational autoencoders to disentangle the latent space of fundus images, with an aim to obtain a more robust and adaptable domain-invariant representation that effectively tackles the domain shift encountered in DR datasets. Despite the simplicity of our approach, we explore the efficacy of this classical method and demonstrate its ability to outperform contemporary state-of-the-art approaches for this task using publicly available datasets. Our findings challenge the prevailing assumption that highly sophisticated methods for DR classification are inherently superior for domain generalization. This highlights the importance of considering simple methods and adapting them to the challenging task of generalizing medical images, rather than solely relying on advanced techniques.

关键词： Domain Generalization Diabetic Retinopathy variational autoencoder

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Learning Optimal Priors for Task-Invariant Representations in variational autoencoders 22

Learning Optimal Priors for Task-Invariant Representations i...

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28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KKD)

作者： Takahashi, Hiroshi Iwata, Tomoharu Kumagai, Atsutoshi Kanai, Sekitoshi Yamada, Masanori Yamanaka, Yuuki Kashima, Hisashi NTT Nairobi Kenya Kyoto Univ Kyoto Japan

ISBN: (纸本)9781450393850

The variational autoencoder (VAE) is a powerful latent variable model for unsupervised representation learning. However, it does not work well in case of insufficient data points. To improve the performance in such situations, the conditional VAE (CVAE) is widely used, which aims to share task-invariant knowledge with multiple tasks through the task-invariant latent variable. In the CVAE, the posterior of the latent variable given the data point and task is regularized by the task-invariant prior, which is modeled by the standard Gaussian distribution. Although this regularization encourages independence between the latent variable and task, the latent variable remains dependent on the task. To reduce this task-dependency, the previous work introduced an additional regularizer. However, its learned representation does not work well on the target tasks. In this study, we theoretically investigate why the CVAE cannot sufficiently reduce the task-dependency and show that the simple standard Gaussian prior is one of the causes. Based on this, we propose a theoretical optimal prior for reducing the task-dependency. In addition, we theoretically show that unlike the previous work, our learned representation works well on the target tasks. Experiments on various datasets show that our approach obtains better task-invariant representations, which improves the performances of various downstream applications such as density estimation and classification.

关键词： variational autoencoder Multi-task learning

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Field-aware variational autoencoders for Billion-scale User Representation Learning 38

Field-aware Variational Autoencoders for Billion-scale User ...

引用

38th IEEE International Conference on Data Engineering (ICDE)

作者： Fan, Ge Zhang, Chaoyun Chen, Junyang Li, Baopu Xu, Zenglin Li, Yingjie Peng, Luyu Gong, Zhiguo Tencent Inc Shenzhen Peoples R China Shenzhen Univ Shenzhen Peoples R China Baidu USA Sunnyvale CA USA Harbin Inst Technol Shenzhen Shenzhen Peoples R China Univ Macau Macau Peoples R China

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

User representation learning plays an essential role in Internet applications, such as recommender systems. Though developing a universal embedding for users is demanding, only few previous works are conducted in an unsupervised learning manner. The unsupervised method is however important as most of the user data is collected without specific labels. In this paper, we harness the unsupervised advantages of variational autoencoders (VAEs), to learn user representation from large-scale, high-dimensional, and multi-field data. We extend the traditional VAE by developing Field-aware VAE (FVAE) to model each feature field with an independent multinomial distribution. To reduce the complexity in training, we employ dynamic hash tables, a batched softmax function, and a feature sampling strategy to improve the efficiency of our method. We conduct experiments on multiple datasets, showing that the proposed FVAE significantly outperforms baselines on several tasks of data reconstruction and tag prediction. Moreover, we deploy the proposed method in real-world applications and conduct online A/B tests in a look-alike system. Results demonstrate that our method can effectively improve the quality of recommendation. To the best of our knowledge, it is the first time that the VAE-based user representation learning model is applied to real-world recommender systems.

关键词： User Representation Learning Recommender Systems Lookalike Systems variational autoencoder

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A Two-Stage Cascade Model with variational autoencoders and Attention Gates for MRI Brain Tumor Segmentation 6th

A Two-Stage Cascade Model with Variational Autoencoders and ...

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6th International MICCAI Brain-Lesion Workshop (BrainLes)

作者： Lyu, Chenggang Shu, Hai NYU Dept Biostat Sch Global Publ Hlth New York NY 10003 USA

ISBN: (纸本)9783030720834;9783030720841

Automatic MRI brain tumor segmentation is of vital importance for the disease diagnosis, monitoring, and treatment planning. In this paper, we propose a two-stage encoder-decoder based model for brain tumor subregional segmentation. variational autoencoder regularization is utilized in both stages to prevent the overfitting issue. The second-stage network adopts attention gates and is trained additionally using an expanded dataset formed by the first-stage outputs. On the BraTS 2020 validation dataset, the proposed method achieves the mean Dice score of 0.9041, 0.8350, and 0.7958, and Hausdorff distance (95%) of 4.953, 6.299, 23.608 for the whole tumor, tumor core, and enhancing tumor, respectively. The corresponding results on the BraTS 2020 testing dataset are 0.8729, 0.8357, and 0.8205 for Dice score, and 11.4288, 19.9690, and 15.6711 for Hausdorff distance. The code is publicly available at https://***/shu-hai/two-stage-VAE-Attention-gate-BraTS2020.

关键词： Attention gate Brain tumor segmentation Encoder-decoder network variational autoencoder

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An Evolutionary Approach to variational autoencoders 10

An Evolutionary Approach to Variational Autoencoders

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10th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Hajewski, Jeff Oliveira, Suely Univ Iowa Dept Comp Sci Iowa City IA 52242 USA

ISBN: (纸本)9781728137834

variational autoencoders are an important tool in the domain of generative data models and yet they remain difficult to design due to a lack of intuition as to how to best design the corresponding encoder and decoder neural networks in addition to determining the size of the latent dimension. Furthermore, there is no definitive guidance on how one should structure these networks. Designing an effective variational autoencoder typically requires fine-tuning and experimenting with different neural network architectures and latent dimensions, which, for large datasets, can be costly both in time and money. In this work we present an approach for designing variational autoencoders based on evolutionary neural architecture search. Our technique is efficient, avoiding redundant computation, and scalable. We explore how the number of epochs used during the neural architecture search affects the properties of the resulting variational autoencoders as well as study the characteristics of the learned latent manifolds. We find that evolutionary search is able to find highly performant network even when the networks are evaluated after only two epochs of training. Using this insight we are able to dramatically reduce the overall computational requirements of our neural architecture search system applied to variational autoencoders.

关键词： variational autoencoder neural architecture search evolutionary algorithm distributed system

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3D HIGH-RESOLUTION CARDIAC SEGMENTATION RECONSTRUCTION FROM 2D VIEWS USING CONDITIONAL variational autoencoderS 16

3D HIGH-RESOLUTION CARDIAC SEGMENTATION RECONSTRUCTION FROM ...

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16th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Biffi, Carlo Cerrolaza, Juan J. Tarroni, Giacomo de Marvao, Antonio Cook, Stuart A. O'Regan, Declan P. Rueckert, Daniel Imperial Coll London Biomed Image Anal Grp London England Imperial Coll London MRC London Inst Med Sci London England

ISBN: (纸本)9781538636411

Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology. High resolution 3D MR sequences enable whole-heart structural imaging but are time-consuming, expensive to acquire and they often require long breath holds that are not suitable for patients. Consequently, multiplanar breath-hold 2D cines sequences are standard practice but are disadvantaged by lack of whole-heart coverage and low through-plane resolution. To address this, we propose a conditional variational autoencoder architecture able to learn a generative model of 3D high-resolution left ventricular (LV) segmentations which is conditioned on three 21) LV segmentations of one short-axis and two long-axis images. By only employing these three 2D segmentations, our model can efficiently reconstruct the 3D high-resolution LV segmentation of a subject. When evaluated on 400 unseen healthy volunteers, our model yielded an average Dice score of 87.92 +/- 0.15 and outperformed competing architectures (TL-net, Dice score = 82.60 +/- 0.23, p = 2.2 . 10(-16)).

关键词： Cardiac MR variational autoencoder 3D Segmentation Reconstruction Deep Learning

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