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检索条件"主题词=Variational autoencoder"

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variational Deep Representation Learning for Cross-Modal Retrieval 4th

Variational Deep Representation Learning for Cross-Modal Ret...

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4th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Yang, Chen Deng, Zongyong Li, Tianyu Liu, Hao Liu, Libo Ningxia Univ Sch Informat Engn Yinchuan 750021 Ningxia Peoples R China Ningxia Municipal & Minist Educ Collaborat Innovat Ctr Ningxia Big Data & Artific Yinchuan 750021 Ningxia Peoples R China

ISBN: (纸本)9783030880071;9783030880064

In this paper, we propose a variational deep representation learning (VDRL) approach for cross-modal retrieval. Numerous existing methods map the image and text to the point representations, which is challenging to model the semantic multiplicity of the sample. To address this issue, our VDRL aims to map the image and text to the semantic distributions and measure the similarity by comparing the difference between their distributions. Specifically, our VDRL network is trained under three constraints: 1) The variational autoencoder loss is minimized to learn the distributions of both the images in image semantic space and the texts in text semantic space. 2) The mutual information is introduced to ensure the VDRL learns the intact distribution for the sample. 3) The triplet hinge loss is incorporated to align the distributions of the images and texts at the semantic level. Consequently, the semantic multiplicity of each sample is modeled in our method. Experimental results demonstrate that our approach achieves compelling performance with state-of-the-art methods.

关键词： Cross-modal retrieval Representation learning variational autoencoder Deep learning

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Self-attention Based Text Matching Model with Generative Pre-training 19

Self-attention Based Text Matching Model with Generative Pre...

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6th IEEE Cyber Science and Technology Congress (CyberSciTech)

作者： Zhang, Xiaolin Lei, Fengpei Yu, Shengji Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China

ISBN: (纸本)9781665421744

Text matching is an important method to judge the semantic similarity of different sentences. Improving the efficiency and accuracy of text matching is the most focus in the field of information matching. In recent years, deep learning has been widely applied to text matching tasks and achieved good results. However, the different models have different limitations, such as CNN cannot learn global semantic information well, RNN cannot be parallelized well, and large pre-training language models have too many parameters to be deployed on hardware well. To address these problems, this paper propose a self-attention based text matching model with generative pre-training. Self-attention mechanism is adopted to learn the semantic information between words in a sentence, and can achieve better parallelization. We use the deep separable convolution model to obtain local features. In the pretraining stage of this model, a generative model variational autoencoder is used to learn the semantic relationship between similar sentences. And in the downstream text matching model, we employ Siamese Network structure, combine depth-wise separable convolutions and self-attention mechanism for feature extraction, and use attention mechanism for text interaction, in which the parameters in the pre-training phase will be shared. At last, we evaluate our model on three datasets: LCQMC, QQP, and a securities dataset. Experiment results show that our method achieves pretty good performance.

关键词： deep learning text matching variational autoencoder depth-wise separable convolutions self-attention

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3D Nucleus Instance Segmentation for Whole-Brain Microscopy Images 27th

3D Nucleus Instance Segmentation for Whole-Brain Microscopy ...

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27th International Conference on Information Processing in Medical Imaging (IPMI)

作者： Ma, Junbo Krupa, Oleh Glass, Madison Rose McCormick, Carolyn M. Borland, David Kim, Minjeong Stein, Jason L. Wu, Guorong Univ North Carolina Chapel Hill Dept Psychiat Chapel Hill NC 27514 USA Univ North Carolina Chapel Hill UNC Neurosci Ctr Chapel Hill NC 27599 USA Univ North Carolina Chapel Hill Dept Genet Chapel Hill NC 27599 USA Univ North Carolina Chapel Hill RENCI Chapel Hill NC 27599 USA Univ North Carolina Greensboro Dept Comp Sci Greensboro NC 27412 USA Univ North Carolina Chapel Hill Dept Comp Sci Chapel Hill NC 27514 USA

ISBN: (纸本)9783030781903;9783030781910

Tissue clearing and light-sheet microscopy technologies offer new opportunities to quantify the three-dimensional (3D) neural structure at a cellular or even sub-cellular resolution. Although many efforts have been made to recognize nuclei in 3D using deep learning techniques, current state-of-the-art approaches often work in a two-step manner, i.e., first segment nucleus regions within a 2D optical slice and then assemble the regions into the 3D instance of a nucleus. Due to the poor inter-slice resolution in many volumetric microscopy images and lack of contextual information across image slices, the current two-step approaches yield less accurate instance segmentation results. To address these limitations, a novel neural network for 3D nucleus instance segmentation (NIS) is proposed, called NIS-Net, which jointly segments and assembles the 3D instances of nuclei. Specifically, a pretext task is designed to predict the image appearance of the to-be-processed slice using the learned context from the processed slices, where the well-characterized contextual information is leveraged to guide the assembly of 3D nuclei instances. Since our NIS-Net progressively identifies nuclei instances by sliding over the entire image stack, our method is capable of segmenting nuclei instances for the whole mouse brain. Experimental results show that our proposed NIS-Net achieves higher accuracy and more reasonable nuclei instances than the current counterpart methods.

关键词： Nucleus instance segmentation Light-sheet microscopy images Self-supervised learning variational autoencoder Deep learning

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Towards speech enhancement using a variational U-Net architecture 29

Towards speech enhancement using a variational U-Net archite...

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29th European Signal Processing Conference (EUSIPCO)

作者： Nustede, Eike J. Anemueller, Joern Carl von Ossietzky Univ Oldenburg Computat Audit Grp Dept Med Phys & Acoust Oldenburg Germany Carl von Ossietzky Univ Oldenburg Computat Audit Grp Cluster Excellence Hearing4all Oldenburg Germany

ISBN: (纸本)9789082797060

We investigate the viability of a variational U-Net architecture for denoising of single-channel audio data. Deep network speech enhancement systems commonly aim to estimate filter masks, or opt to work on the waveform signal, potentially neglecting relationships across higher dimensional spectrotemporal features. We study the adoption of a probabilistic (variational) bottleneck into the classic U-Net architecture for direct spectral reconstruction. Evaluation of several ablation network variants is carried out using signal-to-distortion ratio and perceptual measures, on audio data that includes known and unknown noise types as well as reverberation. Our experiments show that the residual (skip) connections in the proposed system are a prerequisite for successful spectral reconstruction, i.e., without filter mask estimation. Results show, on average, an advantage of the proposed variational U-Net architecture over its classic, non-variational version in signal enhancement performance under reverberant conditions of 0.31 and 6.98 in PESQ and STOI scores, respectively. Anecdotal evidence points to improved suppression of impulsive noise sources with the variational U-Net compared to the recurrent mask estimation network baseline.

关键词： Speech enhancement U-Net architecture variational autoencoder deep learning audio source separation

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Towards Source-Aligned variational Models for Cross-Domain Recommendation 21

Towards Source-Aligned Variational Models for Cross-Domain R...

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15th ACM Conference on Recommender Systems (RECSYS)

作者： Salah, Aghiles Tran, Thanh Binh Lauw, Hady W. Rakuten Inst Technol Paris France Singapore Management Univ Singapore Singapore

ISBN: (纸本)9781450384582

Data sparsity is a long-standing challenge in recommender systems. Among existing approaches to alleviate this problem, cross-domain recommendation consists in leveraging knowledge from a source domain or category (e.g., Movies) to improve item recommendation in a target domain (e.g., Books). In this work, we advocate a probabilistic approach to cross-domain recommendation and rely on variational autoencoders (VAEs) as our latent variable models. More precisely, we assume that we have access to a VAE trained on the source domain that we seek to leverage to improve preference modeling in the target domain. To this end, we propose a model which learns to fit the target observations and align its hidden space with the source latent space jointly. Since we model the latent spaces by the variational posteriors, we operate at this level, and in particular, we investigate two approaches, namely rigid and soft alignments. In the former scenario, the variational model in the target domain is set equal to the source variational model. That is, we only learn a generative model in the target domain. In the soft-alignment scenario, the target VAE has its variational model, but which is encouraged to look like its source counterpart. We analyze the proposed objectives theoretically and conduct extensive experiments to illustrate the benefit of our contribution. Empirical results on six real-world datasets show that the proposed models outperform several comparable cross-domain recommendation models.

关键词： Cross-Domain Recommendation variational autoencoder Collaborative Filtering Neural Networks

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IDA-GAN: A Novel Imbalanced Data Augmentation GAN 25

IDA-GAN: A Novel Imbalanced Data Augmentation GAN

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25th International Conference on Pattern Recognition (ICPR)

作者： Yang, Hao Zhou, Yun Natl Univ Def Technol Sci & Technol Informat Syst Engn Lab Changsha Hunan Peoples R China

ISBN: (纸本)9781728188089

Class imbalance is a widely existed and challenging problem in real-world applications such as disease diagnosis, fraud detection, network intrusion detection and so on. Due to the scarce of data, it could significantly deteriorate the accuracy of classification. To address this challenge, we propose a novel Imbalanced Data Augmentation Generative Adversarial Networks (GAN) named IDA-GAN as an augmentation tool to deal with the imbalanced dataset. This is a great challenge because it is hard to train a GAN model under this situation. We address this issue by coupling variational autoencoder along with GAN training. In this paper, specifically, we introduce the variational autoencoder to learn the majority and minority class distributions in the latent space, and use the generative model to utilize each class distribution for the subsequent GAN training. The generative model learns useful features to generate target minority-class samples. Compared with the state-of-the-art GAN model, the experimental results demonstrate that our proposed IDA-GAN could generate more diverse minority samples with better qualities, and it could benefits the imbalanced classification task in terms of several widely-used evaluation metrics on five benchmark datasets: MNIST, Fashion-MNIST, SVHN, CIFAR-10 and GTSRB.

关键词： Imbalanced learning Data augmentation variational autoencoder GAN

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Controllable Gradient Item Retrieval 21

Controllable Gradient Item Retrieval

引用

30th World Wide Web Conference (WWW)

作者： Wang, Haonan Zhou, Chang Yang, Carl Yang, Hongxia He, Jingrui Univ Illinois Urbana IL USA Alibaba Grp Hangzhou Peoples R China Emory Univ Atlanta GA 30322 USA

ISBN: (纸本)9781450383127

In this paper, we identify and study an important problem of gradient item retrieval. We define the problem as retrieving a sequence of items with a gradual change on a certain attribute, given a reference item and a modification text. For example, after a customer saw a white dress, she/he wants to buy a similar one but more floral on it. The extent of "more floral" is subjective, thus prompting one floral dress is hard to satisfy the customer's needs. A better way is to present a sequence of products with increasingly floral attributes based on the white dress, and allow the customer to select the most satisfactory one from the sequence. Existing item retrieval methods mainly focus on whether the target items appear at the top of the retrieved sequence, but ignore the demand for retrieving a sequence of products with gradual change on a certain attribute. To deal with this problem, we propose a weakly-supervised method that can learn a disentangled item representation from user-item interaction data and ground the semantic meaning of attributes to dimensions of the item representation. Our method takes a reference item and a modification as a query. During inference, we start from the reference item and "walk" along the direction of the modification in the item representation space to retrieve a sequence of items in a gradient manner. We demonstrate our proposed method can achieve disentanglement through weak supervision. Besides, we empirically show that an item sequence retrieved by our method is gradually changed on an indicated attribute and, in the item retrieval task, our method outperforms existing approaches on three different datasets.

关键词： information retrieval recommendation system weakly-supervised learning disentangled representation learning variational autoencoder

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Open-Set Audio Classification with Limited Training Resources based on Augmentation Enhanced variational Auto-Encoder GAN with Detection-Classification Joint Training 22

Open-Set Audio Classification with Limited Training Resource...

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Interspeech Conference

作者： Kah Kuan Teh Huy Dat Tran ASTAR Inst Infocomm Res Aural & Language Intelligence Dept Singapore Singapore

ISBN: (纸本)9781713836902

In this paper, we propose a novel method to address practical problems when deploying audio classification systems in operations that are the presence of unseen sound classes (open-set) and the limitation of training resources. To solve it, a novel method which embeds variational auto-encoder (VAE), data augmentation and detection-classification joint training into conventional GAN networks is proposed. The VAE input to GAN-generator helps to generate realistic outlier samples which are not too far from in-distribution class and hence improve the open-set discrimination capabilities of classifiers. Next, the augmentation enhanced GAN scheme developed in our previous work [4] for close-set audio classification, will help to address the limited training resources by in cooperating the physical data augmentation to work together with traditional GAN produced samples to prevent overfitting and improve the optimization convergences. The detection-classification joint training further steps on advantages of VAE and Augmentation GAN to further improving the performances of detection and classification tasks. The experiments carried out on Google Speech Command database show great improvements of open-set classification accuracy from 62.41% to 88.29% when using only 10% amount of training data.

关键词： Audio Classification Limited Training variational autoencoder Generative Adversarial Networks Open set classification Sinkhorn divergence

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Learning Disentangled Representations with the Wasserstein autoencoder 1

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21st Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Gaujac, Benoit Feige, Ilya Barber, David UCL London England

ISBN: (数字)9783030865238

ISBN: (纸本)9783030865238;9783030865221

Disentangled representation learning has undoubtedly benefited from objective function surgery. However, a delicate balancing act of tuning is still required in order to trade off reconstruction fidelity versus disentanglement. Building on previous successes of penalizing the total correlation in the latent variables, we propose TCWAE (Total Correlation Wasserstein autoencoder). Working in the WAE paradigm naturally enables the separation of the total-correlation term, thus providing disentanglement control over the learned representation, while offering more flexibility in the choice of reconstruction cost. We propose two variants using different KL estimators and analyse in turn the impact of having different ground cost functions and latent regularization terms. Extensive quantitative comparisons on data sets with known generative factors shows that our methods present competitive results relative to state-of-the-art techniques. We further study the trade off between disentanglement and reconstruction on more-difficult data sets with unknown generative factors, where the flexibility of the WAE paradigm leads to improved reconstructions.

关键词： Wasserstein autoencoder variational autoencoder Generative modelling Representation learning Disentanglement learning

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Classification and Authentication of Mineral Water Samples using Electronic Tongue and Deep Neural Networks 3

Classification and Authentication of Mineral Water Samples u...

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3rd IEEE International Conference on Cognitive Machine Intelligence (IEEE CogMI)

作者： Damarla, Seshu Kumar Zhu, Xiuli Kundu, Madhusree Univ Alberta Dept Chem & Mat Engn Edmonton AB Canada Donghua Univ Dept Informat Sci & Technol Shanghai Peoples R China Natl Inst Technol Rourkela Dept Chem Engn Rourkela Odisha India

ISBN: (纸本)9781665416214

Supervised multiclass classifiers based on deep neural networks (one-dimensional convolutional neural network (1D-CNN) and long short-term network (LSTM)) are developed to classify and authenticate mineral water samples of commercial brands (Aquafina, Bisleri, Oasis, Kingfisher, Dolphin and McDowell) available in Indian market. Electronic tongue based experiments are conducted to generate output waveforms (current signals) of the water samples of the six brands. Since the data obtained via the experiments are not adequate to train the deep neural networks, variational autoencoder (VAE) is used to generate additional current signals for each of the six brands. New database consisting of the experimental data and data generated using VAE is utilized to train and test the classifiers. Classification accuracies obtained by the LSTM and 1D-CNN models are 83.33% and 66.66%, respectively.

关键词： electronic tongue long short-term network convolutional neural network variational autoencoder multiclass classifier

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