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检索条件"主题词=Variational autoencoder"

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Deep Semantic Text Hashing with Weak Supervision 18

Deep Semantic Text Hashing with Weak Supervision

引用

41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Chaidaroon, Suthee Ebesu, Travis Fang, Yi Santa Clara Univ Dept Comp Engn Santa Clara CA 95053 USA

ISBN: (纸本)9781450356572

With an ever increasing amount of data available on the web, fast similarity search has become the critical component for large-scale information retrieval systems. One solution is semantic hashing which designs binary codes to accelerate similarity search. Recently, deep learning has been successfully applied to the semantic hashing problem and produces high-quality compact binary codes compared to traditional methods. However, most state-of-the-art semantic hashing approaches require large amounts of hand-labeled training data which are often expensive and time consuming to collect. The cost of getting labeled data is the key bottleneck in deploying these hashing methods. Motivated by the recent success in machine learning that makes use of weak supervision, we employ unsupervised ranking methods such as BM25 to extract weak signals from training data. We further introduce two deep generative semantic hashing models to leverage weak signals for text hashing. The experimental results on four public datasets show that our models can generate high-quality binary codes without using hand-labeled training data and significantly outperform the competitive unsupervised semantic hashing baselines.

关键词： Semantic Hashing Weak Supervision variational autoencoder

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End-to-End Quality Controllable Image Compression

End-to-End Quality Controllable Image Compression

引用

Picture Coding Symposium (PCS)

作者： Wang, Luge Mao, Xionghui Zhang, Saiping Yang, Fuzheng Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China

ISBN: (纸本)9781665492577

Quality control is an important topic in many application scenarios such as medical image compression. To achieve quality control in image compression network, in this paper, we propose a quality controllable image compression network, Quality Controllable variational autoencoder (QC-VAE). QC-VAE consists of the Quality-Feature-Level (QFL) model we proposed and the Hyperprior Continuously Variable Rate (HCVR) image compression network which can adapt to multiple target qualities with only one single model. Note that even if the target quality is the same, different images should be quantized with different levels. With the help of the QFL model, we can obtain the estimated corresponding quantization level of the input image under the target quality, the selected level will be used to control the quantization loss. By this means, the QC-VAE can adapt to the target quality with high accuracy. Experimental results have shown that compared with the HCVR model, the proposed QC-VAE achieves accurate quality control without ratedistortion (RD) performance loss, indicating its superiority.

关键词： Deep image compression quality control variational autoencoder variable rate

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Multimodal Brain Tumor Segmentation with Normal Appearance autoencoder 5th

Multimodal Brain Tumor Segmentation with Normal Appearance A...

引用

5th International MICCAI Brain-Lesion Workshop (BrainLes)

作者： Astaraki, Mehdi Wang, Chunliang Carrizo, Gabriel Toma-Dasu, Iuliana Smedby, Orjan KTH Royal Inst Technol Dept Biomed Engn & Hlth Syst Halsovagen 11C S-14157 Huddinge Sweden Karolinska Univ Sjukhuset Karolinska Inst Dept Oncol Pathol S-17176 Solna Sweden

ISBN: (纸本)9783030466428;9783030466435

We propose a hybrid segmentation pipeline based on the autoencoders' capability of anomaly detection. To this end, we, first, introduce a new augmentation technique to generate synthetic paired images. Gaining advantage from the paired images, we propose a Normal Appearance autoencoder (NAA) that is able to remove tumors and thus reconstruct realistic-looking, tumor-free images. After estimating the regions where the abnormalities potentially exist, a segmentation network is guided toward the candidate region. We tested the proposed pipeline on the BraTS 2019 database. The preliminary results indicate that the proposed model improved the segmentation accuracy of brain tumor subregions compared to the U-Net model.

关键词： Brain tumor segmentation variational autoencoder Paired-image generation

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Semi-Supervised Multi-modal Sensor Fusion Framework for In-Vehicle Networks

Semi-Supervised Multi-modal Sensor Fusion Framework for In-V...

引用

19th Workshop on Mobility in the Evolving Internet Architecture

作者： Zheng, Zijian Yi, Wenqiang Nallanathan, Arumugam Queen Mary Univ London London England Univ Essex Colchester Essex England

ISBN: (纸本)9798400712470

With the rapid development of technologies such as autonomous driving, vehicle-to-everything communication, and edge computing, an increasing number of vehicles are equipped with multiple sensors to perceive the surroundings. As a result, the amount of sensing data has exploded, and the communication pressure on the in-vehicle network becomes severe. In-sensor or near-sensor computation is considered an effective method to address these issues. However, current multi-modal fusion frameworks are challenging to be modularised and trained in a distributed manner across multiple devices. In this paper, we propose a variational autoencoder (VAE) based multi-modal fusion solution with its theoretical analysis framework. Notably, we design two auxiliary tasks to utilize data from a single modality to discover the joint distribution of multiple modalities. Compared to traditional algorithms, the proposed solution is able to use unlabeled data for self-supervised learning and has the added advantage of modularity, which helps to reduce the communication overhead in in-vehicle networks. Experiments show that, compared to single-modality algorithms, our multi-modal fusion framework increases average precision by over 10% on the KITTI dataset.

关键词： In-vehicle networks multi-modality learning variational autoencoder

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Evaluation of patient re-identification using laboratory test orders and mitigation via latent space variables 24

Evaluation of patient re-identification using laboratory tes...

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24th Pacific Symposium on Biocomputing (PSB)

作者： Johnson, Kipp W. De Freitas, Jessica K. Glicksberg, Benjamin S. Bobe, Jason R. Dudley, Joel T. Icahn Sch Med Mt Sinai Dept Genet & Genom Sci Inst Next Generat Healthcare 770 Lexington Ave15th Fl New York NY 10065 USA Univ Calif San Francisco Bakar Computat Hlth Sci Inst San Francisco CA 10065 USA

ISBN: (纸本)9789813279827;9789813279810

Anonymized electronic health records (EHR) are often used for biomedical research. One persistent concern with this type of research is the risk for re-identification of patients from their purportedly anonymized data. Here, we use the EHR of 731,850 de-identified patients to demonstrate that the average patient is unique from all others 98.4% of the time simply by examining what laboratory tests have been ordered for them. By the time a patient has visited the hospital on two separate days, they are unique in 72.3% of cases. We further present a computational study to identify how accurately the records from a single day of care can be used to re-identify patients from a set of 99 other patients. We show that, given a single visit's laboratory orders (even without result values) for a patient, we can re-identify the patient at least 25% of the time. Furthermore, we can place this patient among the top 10 most similar patients 47% of the time. Finally, we present a proof-of-concept technique using a variational autoencoder to encode laboratory results into a lower-dimensional latent space. We demonstrate that releasing latent-space encoded laboratory orders significantly improves privacy compared to releasing raw laboratory orders (<5% re-identification), while preserving information contained within the laboratory orders (AUC of >0.9 for recreating encoded values). Our findings have potential consequences for the public release of anonymized laboratory tests to the biomedical research community. We note that our findings do not imply that laboratory tests alone are personally identifiable. In the attack scenario presented here, reidentification would require a threat actor to possess an external source of laboratory values which are linked to personal identifiers at the start.

关键词： Electronic health records anonymization patient re-identification data privacy variational autoencoder

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JOINT DISENTANGLEMENT OF LABELS AND THEIR FEATURES WITH VAE 29

JOINT DISENTANGLEMENT OF LABELS AND THEIR FEATURES WITH VAE

引用

IEEE International Conference on Image Processing (ICIP)

作者： Zou, Kaifeng Faisan, Sylvain Heitz, Fabrice Valette, Sebastien Univ Strasbourg ICube Lab Strasbourg France INSA Lyon CREATIS Lyon France

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Most of previous semi-supervised methods that seek to obtain disentangled representations using variational autoencoders divide the latent representation into two components: the non-interpretable part and the disentangled part that explicitly models the factors of interest. With such models, features associated with high-level factors are not explicitly modeled, and they can either be lost, or at best entangled in the other latent variables, thus leading to bad disentanglement properties. To address this problem, we propose a novel conditional dependency structure where both the labels and their features belong to the latent space. We show using the CelebA dataset that the proposed model can learn meaningful representations, and we provide quantitative and qualitative comparisons with other approaches that show the effectiveness of the proposed method.

关键词： disentangled representation variational autoencoder

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Transfer learning of the expressivity using FLOW metric learning in multispeaker text-to-speech synthesis 21

Transfer learning of the expressivity using FLOW metric lear...

引用

Interspeech Conference

作者： Kulkarni, Ajinkya Colotte, Vincent Jouvet, Denis Univ Lorraine CNRS INRIA LORIA F-54000 Nancy France

ISBN: (纸本)9781713820697

In this paper, we present a novel flow metric learning architecture in a parametric multispeaker expressive text-to-speech (TTS) system. We proposed inverse autoregressive flow (IAF) as a way to perform the variational inference, thus providing flexible approximate posterior distribution. The proposed approach condition the text-to-speech system on speaker embeddings so that latent space represents the emotion as semantic characteristics. For representing the speaker, we extracted speaker embeddings from the x-vector based speaker recognition model trained on speech data from many speakers. To predict the vocoder features, we used the acoustic model conditioned on the textual features as well as on the speaker embedding. We transferred the expressivity by using the mean of the latent variables for each emotion to generate expressive speech in different speaker's voices for which no expressive speech data is available. We compared the results obtained using flow-based variational inference with variational autoencoder as a baseline model. The performance measured by mean opinion score (MOS), speaker MOS, and expressive MOS shows that N-pair loss based deep metric learning along with IAF model improves the transfer of expressivity in the desired speaker's voice in synthesized speech.

关键词： text-to-speech variational autoencoder inverse autoregressive flow deep metric learning expressivity

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Cardiac MRI Segmentation with Strong Anatomical Guarantees 22nd

Cardiac MRI Segmentation with Strong Anatomical Guarantees

引用

10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Painchaud, Nathan Skandarani, Youssef Judge, Thierry Bernard, Olivier Lalande, Alain Jodoin, Pierre-Marc Univ Sherbrooke Dept Comp Sci Sherbrooke PQ Canada Univ Bourgogne Franche Comte Dijon France Univ Lyon Lyon France

ISBN: (纸本)9783030322458;9783030322441

Recent publications have shown that the segmentation accuracy of modern-day convolutional neural networks (CNN) applied on cardiac MRI can reach the inter-expert variability, a great achievement in this area of research. However, despite these successes, CNNs still produce anatomically inaccurate segmentations as they provide no guarantee on the anatomical plausibility of their outcome, even when using a shape prior. In this paper, we propose a cardiac MRI segmentation method which always produces anatomically plausible results. At the core of the method is an adversarial variational autoencoder (aVAE) whose latent space encodes a smooth manifold on which lies a large spectrum of valid cardiac shapes. This aVAE is used to automatically warp anatomically inaccurate cardiac shapes towards a close but correct shape. Our method can accommodate any cardiac segmentation method and convert its anatomically implausible results to plausible ones without affecting its overall geometric and clinical metrics. With our method, CNNs can now produce results that are both within the inter-expert variability and always anatomically plausible.

关键词： CNN variational autoencoder Cardiac MRI segmentation

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LEARNING LATENT REPRESENTATIONS FOR STYLE CONTROL AND TRANSFER IN END-TO-END SPEECH SYNTHESIS 44

LEARNING LATENT REPRESENTATIONS FOR STYLE CONTROL AND TRANSF...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Zhang, Ya-Jie Pan, Shifeng He, Lei Ling, Zhen-Hua Univ Sci & Technol China Natl Engn Lab Speech & Language Informat Proc Hefei Anhui Peoples R China Microsoft China Beijing Peoples R China Microsoft STC Asia Beijing Peoples R China

ISBN: (纸本)9781479981311

In this paper, we introduce the variational autoencoder (VAE) to an end-to-end speech synthesis model, to learn the latent representation of speaking styles in an unsupervised manner. The style representation learned through VAE shows good properties such as disentangling, scaling, and combination, which makes it easy for style control. Style transfer can be achieved in this framework by first inferring style representation through the recognition network of VAE, then feeding it into TTS network to guide the style in synthesizing speech. To avoid Kullback-Leibler (KL) divergence collapse in training, several techniques are adopted. Finally, the proposed model shows good performance of style control and outperforms Global Style Token (GST) model in ABX preference tests on style transfer.

关键词： unsupervised learning variational autoencoder style transfer speech synthesis

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Learning Disentangled User Representation Based on Controllable VAE for Recommendation 26th

Learning Disentangled User Representation Based on Controlla...

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26th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Li, Yunyi Zhao, Pengpeng Wang, Deqing Xian, Xuefeng Liu, Yanchi Sheng, Victor S. Soochow Univ Inst Artificial Intelligence Sch Comp Sci & Technol Suzhou Peoples R China Beihang Univ Suzhou Peoples R China Suzhou Vocat Univ Suzhou Peoples R China NEC Labs Amer Princeton NJ USA Texas Tech Univ Dept Comp Sci Lubbock TX USA

ISBN: (纸本)9783030731991;9783030732004

User behaviour on purchasing is always driven by complex latent factors, which are highly disentangled in the real world. Learning latent factorized representation of users can uncover user intentions behind the observed data (i.e. user-item interaction) and improve the robustness and interpretability of the recommender system. However, existing collaborative filtering methods learning disentangled representation face problems of balancing the trade-off between reconstruction quality and disentanglement. In this paper, we propose a controllable variational autoencoder framework for collaborative filtering. Specifically, we adopt a modified Proportional-Integral-Derivative (PID) control to the beta-VAE objective to automatically tune the hyperparameter beta using the output of Kullback-Leibler divergence as feedback. We further introduce item embeddings to guide the system to learn representation related to the real-world concepts using a factorized Gaussian distribution. Experimental results show that our model can get a crucial improvement over state-of-the-art baselines. We further evaluate our model's effectiveness to control the trade-off between reconstruction error and disentanglement quality in the recommendation.

关键词： variational autoencoder Disentangled representation learning Recommender system

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