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检索条件"主题词=Variational autoencoder"

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3D Fetal Skull Reconstruction from 2DUS via Deep Conditional Generative Networks 21st

3D Fetal Skull Reconstruction from 2DUS via Deep Conditional...

引用

21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

作者： Cerrolaza, Juan J. Li, Yuanwei Biffi, Carlo Gomez, Alberto Sinclair, Matthew Matthew, Jacqueline Knight, Caronline Kainz, Bernhard Rueckert, Daniel Imperial Coll London Biomed Image Anal Grp London England Kings Coll London Div Imaging Sci & Biomed Engn London England

ISBN: (纸本)9783030009281;9783030009274

2D ultrasound (US) is the primary imaging modality in antenatal healthcare. Despite the limitations of traditional 2D biometrics to characterize the true 3D anatomy of the fetus, the adoption of 3DUS is still very limited. This is particularly significant in developing countries and remote areas, due to the lack of experienced sonographers and the limited access to 3D technology. In this paper, we present a new deep conditional generative network for the 3D reconstruction of the fetal skull from 2DUS standard planes of the head routinely acquired during the fetal screening process. Based on the generative properties of conditional variational autoencoders (CVAE), our reconstruction architecture (REC-CVAE) directly integrates the three US standard planes as conditional variables to generate a unified latent space of the skull. Additionally, we propose HiREC-CVAE, a hierarchical generative network based on the different clinical relevance of each predictive view. The hierarchical structure of HiREC-CVAE allows the network to learn a sequence of nested latent spaces, providing superior predictive capabilities even in the absence of some of the 2DUS scans. The performance of the proposed architectures was evaluated on a dataset of 72 cases, showing accurate reconstruction capabilities from standard non-registered 2DUS images.

关键词： Generative model variational autoencoder Fetal ultrasound

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Unsupervised Adaptation with Interpretable Disentangled Representations for Distant Conversational Speech Recognition 19

Unsupervised Adaptation with Interpretable Disentangled Repr...

引用

19th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2018)

作者： Hsu, Wei-Ning Tang, Hao Glass, James MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781510872219

The current trend in automatic speech recognition is to leverage large amounts of labeled data to train supervised neural network models. Unfortunately, obtaining data for a wide range of domains to train robust models can be costly. However, it is relatively inexpensive to collect large amounts of unlabeled data from domains that we want the models to generalize to. In this paper, we propose a novel unsupervised adaptation method that learns to synthesize labeled data for the target domain from unlabeled in-domain data and labeled out-of-domain data. We first learn without supervision an interpretable latent representation of speech that encodes linguistic and nuisance factors (e.g., speaker and channel) using different latent variables. To transform a labeled out-of-domain utterance without altering its transcript, we transform the latent nuisance variables while maintaining the linguistic variables. To demonstrate our approach, we focus on a channel mismatch setting, where the domain of interest is distant conversational speech, and labels are only available for close-talking speech. Our proposed method is evaluated on the AMI dataset, outperforming all baselines and bridging the gap between unadapted and in-domain models by over 77% without using any parallel data.

关键词： unsupervised adaptation distant speech recognition unsupervised data augmentation variational autoencoder

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Generation of Reference Trajectories for Safe Trajectory Planning 27th

Generation of Reference Trajectories for Safe Trajectory Pla...

引用

27th International Conference on Artificial Neural Networks (ICANN)

作者： Chaulwar, Amit Botsch, Michael Utschick, Wolfgang Ingolstadt Univ Appl Sci Fac Elect Engn Ingolstadt Germany Tech Univ Munich Dept Elect Engn Munich Germany

ISBN: (纸本)9783030014186

Many variants of a sampling-based motion planning algorithm, namely Rapidly-exploring Random Tree, use biased-sampling for faster convergence. One of such recently proposed variant, the Hybrid-Augmented CL-RRT+, uses a predicted predefined template trajectory with a machine learning algorithm as a reference for the biased sampling. Because of the finite number of template trajectories, the convergence time is short only in scenarios where the final trajectory is close to predicted template trajectory. Therefore, a generative model using variational autoencoder for generating many reference trajectories and a 3D-ConvNet regressor for predicting those reference trajectories for critical vehicle traffic-scenarios is proposed in this work. Using this framework, two different safe trajectory planning algorithms, namely GATE and GATE-ARRT+, are presented in this paper. Finally, the simulation results demonstrate the effectiveness of these algorithms for the trajectory planning task in different types of critical vehicle traffic-scenarios.

关键词： Safe trajectory planning Hybrid machine learning variational autoencoder

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Deep Semantic Text Hashing with Weak Supervision 18

Deep Semantic Text Hashing with Weak Supervision

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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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Deep variational auto-encoders for unsupervised glomerular classification

Deep variational auto-encoders for unsupervised glomerular c...

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SPIE Medical Imaging Symposium / 6th Digital Pathology Conference

作者： Lutnick, Brendon Yacoub, Rabi Jen, Kuang-Yu Tomaszewski, John E. Jain, Sanjay Sarder, Pinaki SUNY Buffalo Dept Pathol & Anat Sci Buffalo NY 14260 USA SUNY Buffalo Med Nephrol Buffalo NY USA Univ Calif Davis Dept Pathol Davis CA 95616 USA Washington Univ Sch Med Dept Med Nephrol St Louis MO 63130 USA

ISBN: (数字)9781510616523

ISBN: (纸本)9781510616523

The adoption of deep learning techniques in medical applications has thus far been limited by the availability of the large labeled datasets required to robustly train neural networks, as well as difficulty interpreting these networks. However, recent techniques for unsupervised training of neural networks promise to address these issues, leveraging only structure to model input data. We propose the use of a variational autoencoder (VAE) which utilizes data from an animal model to augment the training set and non-linear dimensionality reduction to map this data to human sets. This architecture utilizes variational inference, performed on latent parameters, to statistically model the probability distribution of training data in a latent feature space. We show the feasibility of VAEs, using images of mouse and human renal glomeruli from various pathological stages of diabetic nephropathy (DN), to model the progression of structural changes which occur in DN. When plotted in a 2-dimentional latent space, human and mouse glomeruli, show separation with some overlap, suggesting that the data is continuous, and can be statistically correlated. When DN stage is plotted in this latent space, trends in disease pathology are visualized.

关键词： Cross species modeling knowledge transfer variational autoencoder unsupervised modeling

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Integrating Topic Information into VAE for Text Semantic Similarity 1

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25th International Conference on Neural Information Processing (ICONIP)

作者： Su, Xiangdong Yan, Rong Gong, Zheng Fu, Yujiao Xu, Heng Inner Mongolia Univ Coll Comp Sci Hohhot 010021 Peoples R China Inner Mongolia Key Lab Mongolian Informat Proc Te Hohhot 010021 Peoples R China

ISBN: (数字)9783030042219

ISBN: (纸本)9783030042219;9783030042202

Representation learning is an essential process in the text similarity task. The methods based on neural variational inference first learn the semantic representation of the texts, and then measure the similar degree of these texts by calculating the cosine of their representations. However, it is not generally desirable that using the neural network simply to learn semantic representation as it cannot capture the rich semantic information completely. Considering that the similarity of context information reflects the similarity of text pairs in most cases, we integrate the topic information into a stacked variational autoencoder in process of text representation learning. The improved text representations are used in text similarity calculation. Experiment shows that our approach obtains the state-of-art performance.

关键词： Representation learning variational autoencoder Topic information Semantic similarity

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Deep Incremental Learning for Efficient High-Fidelity Face Tracking

Deep Incremental Learning for Efficient High-Fidelity Face T...

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11th ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SA)

作者： Wu, Chenglei Shiratori, Takaaki Sheikh, Yaser Facebook Real Labs Pittsburgh PA 15213 USA

ISBN: (纸本)9781450360081

In this paper, we present an incremental learning framework for efficient and accurate facial performance tracking. Our approach is to alternate the modeling step, which takes tracked meshes and texture maps to train our deep learning-based statistical model, and the tracking step, which takes predictions of geometry and texture our model infers from measured images and optimize the predicted geometry by minimizing image, geometry and facial landmark errors. Our Geo-Tex VAE model extends the convolutional variational autoencoder for face tracking, and jointly learns and represents deformations and variations in geometry and texture from tracked meshes and texture maps. To accurately model variations in facial geometry and texture, we introduce the decomposition layer in the Geo-Tex VAE architecture which decomposes the facial deformation into global and local components. We train the global deformation with a fully-connected network and the local deformations with convolutional layers. Despite running this model on each frame independently - thereby enabling a high amount of parallelization - we validate that our framework achieves sub-millimeter accuracy on synthetic data and outperforms existing methods. We also qualitatively demonstrate high-fidelity, long-duration facial performance tracking on several actors.

关键词： facial performance tracking variational autoencoder

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Neural Collaborative Filtering: Hybrid Recommendation Algorithm with Content Information and Implicit Feedback 19th

Neural Collaborative Filtering: Hybrid Recommendation Algori...

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19th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Ji, Li Lin, Guangyan Tan, Huobin Beihang Univ Sch Software Beijing Peoples R China

ISBN: (纸本)9783030034931;9783030034924

Collaborative filtering methods are widely used for recommender systems. However the performance degrades significantly because of highly sparse rating information in practical situations. Recently many hybrid models incorporating content information have been proposed to alleviate data sparsity. This paper first proposes the collaborative variational ranking model (CVRank) that combines variational autoencoder with pairwise ranking based collaborative filtering. The model learns latent factors of users and items from both rating and content information, and makes recommendation by calculating dot products between users and items. CVRank is suitable for other deep learning models, and can be easily extended to other multimedia other than text. Then this paper sums up similar models as neural collaborative filtering and presents a generic optimization criterion for them. Experiments show that CVRank achieves robust performance under different sparsity level, and neural collaborative filtering methods can gain greater recommendation accuracy improvement compared with pure collaborative filtering when the training data is sparser.

关键词： Collaborative filtering variational autoencoder Recommender system

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A Tensorflow Based Feature Learning Method Application in Fault Detecting of Tract Motor 30

A Tensorflow Based Feature Learning Method Application in Fa...

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30th Chinese Control and Decision Conference (CCDC)

作者： Wang Huizhong Qiao Linhan He Keke Lanzhou Univ Technol Lanzhou 730050 Peoples R China

ISBN: (纸本)9781538612446

In purpose of detecting the inner and outer ring faults of tractor motor, one Feature Learning method, variational autoencoder, which based on Tensorflow, was cited to process the motor vibration signal. This method firstly normalized all data sets Next, these data sets were input into the built variational autoencoder model to train the weights and biases as the feature learning it going on. Then, a Softmax Regression model is used for multi-faults diagnosis. The final results showed that this method can be used fo: finishing multi-faults detecting missions excellently, and for every metric, the results are better than traditional Back Propagation Neura Network, from 87.51% to 93.61%. hence, this unsupervised feature learning method decreased lots of Machine learning model's dependency on feature engineering. It would he a good guidance of actual projects.

关键词： Tensorflow variational autoencoder Softmax Regression Feature Learning Fault Detection

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HAVAE: Learning Prosodic-Enhanced Representations of Rap Lyrics 1

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15th Pacific Rim International Conference on Artificial Intelligence (PRICAI) / 15th Pacific Rim Knowledge Acquisition Workshop (PKAW)

作者： Liang, Hongru Li, Qian Wang, Haozheng Li, Hang Wang, Jun Sun, Zhe Wei, Jin-Mao Yang, Zhenglu Nankai Univ Coll Comp & Control Engn Tianjin Peoples R China Ludong Univ Coll Math & Stat Sci Yantai Peoples R China RIKEN Head Off Informat Syst & Cybersecur Computat Engn Applicat Unit Saitama Japan

ISBN: (数字)9783319973043

ISBN: (纸本)9783319973043;9783319973036

Learning and analyzing rap lyrics is a significant basis for many applications, such as music recommendation, automatic music categorization, and music information retrieval. Although numerous studies have explored the topic, knowledge in this field is far from satisfactory, because critical issues, such as prosodic information and its effective representation, as well as appropriate integration of various features are usually ignored. In this paper, we propose a hierarchical attention variational autoencoder framework (HAVAE), which simultaneously consider semantic and prosodic features for rap lyrics representation learning. Specifically, the representation of the prosodic features is encoded by phonetic transcriptions with a novel and effective strategy (i.e., rhyme2vec). Moreover, a feature aggregation strategy is proposed to appropriately integrate various features and generate prosodic-enhanced representation. A comprehensive empirical evaluation demonstrates that the proposed framework outperforms the state-of-the-art approaches under various metrics in both NextLine prediction task and rap genre classification task.

关键词： Representation learning variational autoencoder Hierarchical attention mechanism

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