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检索条件"主题词=Variational autoencoder"

共 1532 条记录，以下是1221-1230 订阅

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Integrating Topic Information into VAE for Text Semantic Similarity 1

引用

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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Style Transfer of Abstract Drum Patterns Using a Light-Weight Hierarchical autoencoder 1

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30th Benelux Conference on Artificial Intelligence (BNAIC)

作者： Voschezang, Mark Vrije Univ Amsterdam NL-1081 HV Amsterdam Netherlands

ISBN: (数字)9783030319786

ISBN: (纸本)9783030319786;9783030319779

Many improvements have been made in the field of generative modelling. State-of-the-art unsupervised models have been able to transfer the style of existing media with photo-realistic quality. However, these improvements have been largely limited to graphical data. Music has been proven to be more difficult to model. Magenta's MusicVAE can quite successfully generate abstract rhythms and melodies. However, MusicVAE is a large model that requires vast amounts of computing power before it starts to make realistic predictions. Moreover, its input is heavily quantized which makes it impossible to model musical variations such as swing. This paper proposes a lightweight but high-resolution variational recurrent autoencoder that can be used to transfer the style of input samples while maintaining characteristics of the original sample. This model can be trained in a few hours on small datasets and allows researchers and musicians to experiment with musical style transfer. In addition, a novel technique based on normalized compression distance is used to evaluate the model by measuring the similarity of generated samples to target classes.

关键词： variational autoencoder MIDI drum patterns Generative modelling Normalized compression distance

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Detection of Abnormal Folding Patterns with Unsupervised Deep Generative Models 4th

Detection of Abnormal Folding Patterns with Unsupervised Dee...

引用

4th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN)

作者： Guillon, Louise Cagna, Bastien Dufumier, Benoit Chavas, Joel Riviere, Denis Mangin, Jean-Francois Univ Paris Saclay CEA CNRS NeuroSpinBaobab Gif Sur Yvette France IPParis Telecom Paris LTCI Palaiseau France

ISBN: (纸本)9783030875862;9783030875855

Although the main structures of cortical folding are present in each human brain, the folding pattern is unique to each individual. Because of this large normal variability, the identification of abnormal patterns associated to developmental disorders is a complex open challenge. In this paper, we tackle this problem as an anomaly detection task and explore the potential of deep generative models using benchmarks made up of synthetic anomalies. To focus learning on the folding geometry, brain MRI are preprocessed first to deal only with a skeleton-based negative cast of the cortex. A variational auto-encoder is trained to get a representation of the regional variability of the folding pattern of the general population. Then several synthetic benchmark datasets of abnormalities are designed. The latent space expressivity is assessed through classification experiments between control's and abnormal's latent codes. Finally, the properties encoded in the latent space are analyzed through perturbation of specific latent dimensions and observation of the resulting modification of the reconstructed images. The results have shown that the latent representation is rich enough to distinguish subtle differences like asymmetries between the right and left hemispheres.

关键词： variational autoencoder Brain architecture Cortical folding Anomaly benchmark Anomaly detection

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A CLOSER LOOK AT autoencoderS FOR UNSUPERVISED ANOMALY DETECTION 47

A CLOSER LOOK AT AUTOENCODERS FOR UNSUPERVISED ANOMALY DETEC...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Oyedotun, Oyebade K. Aouada, Djamila Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust Snt L-1855 Luxembourg Luxembourg

ISBN: (纸本)9781665405409

Unsupervised anomaly detection is a challenging problem, where the aim is to detect irregular data instances. Interestingly, generative models can learn data distribution, and thus have been proposed for anomaly detection. In this direction, the variational autoencoder (VAE) is popular, as it enforces an explicit probabilistic interpretation of the latent space. We note that there are other generative autoencoders (AEs) such as the denoising AE (DAE) and contractive AE (CAE), which also model data generation process without enforcing an explicit probabilistic latent space interpretation. While it is intuitively straightforward to see the benefit of a latent space with explicit probabilistic interpretation for generative tasks, it is unclear how this can be crucial for anomaly detection problems. Consequently, our exposition in this paper is to investigate the extent to which different latent space attributes of AEs impact their performances for anomaly detection tasks. We take the conventional and deterministic AE that we refer to as plain AE (PAE) as the baseline for performance comparison. Our results obtained using five different datasets reveal that an explicit probabilistic latent space is not necessary for good performance. The best results on most of the datasets are obtained using CAE, which enjoys stable latent representations.

关键词： Anomaly detection autoencoder variational autoencoder latent representations

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The SJTU Robust Anti-spoofing System for the ASVspoof 2019 Challenge 20

The SJTU Robust Anti-spoofing System for the ASVspoof 2019 C...

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Interspeech Conference

作者： Yang, Yexin Wang, Hongji Dinkel, Heinrich Chen, Zhengyang Wang, Shuai Qian, Yanmin Yu, Kai Shanghai Jiao Tong Univ Dept Comp Sci & Engn SpeechLab MoE Key Lab Artificial Intelligence Shanghai Peoples R China

The robustness of an anti-spoofing system is progressively more important in order to develop a reliable speaker verification system. Previous challenges and datasets mainly focus on a specific type of spoofing attacks. The ASVspoof 2019 edition is the first challenge to address two major spoofing types - logical and physical access. This paper presents the SJTU's submitted anti-spoofing system to the ASVspoof 2019 challenge. Log-CQT features are developed in conjunction with multi-layer convolutional neural networks for robust performance across both subtasks. CNNs with gradient linear units (GLU) activations are utilized for spoofing detection. The proposed system shows consistent performance improvement over all types of spoofing attacks. Our primary submissions achieve the 5(th) and 8(th) positions for the logical and physical access respectively. Moreover, our contrastive submission to the PA task exhibits better generalization compared to our primary submission, and achieves a comparable performance to the 3(rd) position of the challenge.

关键词： anti-spoofing spoofing detection variational autoencoder convolutional neural network

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Skip the Benchmark: Generating System-Level High-Level Synthesis Data using Generative Machine Learning 24

Skip the Benchmark: Generating System-Level High-Level Synth...

引用

34th Great Lakes Symposium on VLSI (GLSVLSI)

作者： Liao, Yuchao Adegbija, Tosiron Lysecky, Roman Tandon, Ravi Univ Arizona Elect & Comp Engn Tucson AZ 85721 USA

ISBN: (纸本)9798400706059

High-Level Synthesis (HLS) Design Space Exploration (DSE) is a widely accepted approach for efficiently exploring Pareto-optimal and optimal hardware solutions during the HLS process. Several HLS benchmarks and datasets are available for the research community to evaluate their methodologies. Unfortunately, these resources are limited and may not be sufficient for complex, multi-component system-level explorations. Generating new data using existing HLS benchmarks can be cumbersome, given the expertise and time required to effectively generate data for different HLS designs and directives. As a result, synthetic data has been used in prior work to evaluate system-level HLS DSE. However, the fidelity of the synthetic data to real data is often unclear, leading to uncertainty about the quality of system-level HLS DSE. This paper proposes a novel approach, called Vaegan, that employs generative machine learning to generate synthetic data that is robust enough to support complex system-level HLS DSE experiments that would be unattainable with only the currently available data. We explore and adapt a variational autoencoder (VAE) and Generative Adversarial Network (GAN) for this task and evaluate our approach using state-of-the-art datasets and metrics. We compare our approach to prior works and show that Vaegan effectively generates synthetic HLS data that closely mirrors the ground truth's distribution.

关键词： High-Level Synthesis Synthetic Data Generation variational autoencoder Generative Adversarial Network

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Generalized Zero-Shot Learning Based on Manifold Alignment 16

Generalized Zero-Shot Learning Based on Manifold Alignment

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16th IEEE International Conference on Signal Processing (ICSP)

作者： Xu, Rui Shao, Shuai Liu, Baodi Liu, Weifeng China Univ Petr East China Coll Control Sci & Engn Qingdao Peoples R China Zhejiang Lab Hangzhou Peoples R China

ISBN: (数字)9781665460569

ISBN: (纸本)9781665460569

Generalized zero-shot learning is a method that can classify seen and unseen samples by learning training samples' visual and semantic modal information. Visual modal information is generally extracted by feature extraction networks pre-trained with a large-scale data set, and semantic modal information is typically represented by class attributes. Different categories have shared semantic information, therefore, through learning the mapping between two modal information, the transferable knowledge can be used to classify testing samples. However, most methods align the two modal information of the per-sample rather than considering the alignment of the distribution of multiple instances in the two modalities. We utilize variational autoencoders mapping two modalities' information to a shared latent space, then align the samples' manifold structure of them to promote the accuracy of model classification. We evaluate the proposed method on several benchmark datasets (CUB, SUN, and AWA2), and the significant improvements have proved the method's effectiveness.

关键词： manifold alignment variational autoencoder generalized zero-shot learning

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Encoding High-Level Features: An Approach To Robust Transfer Learning

Encoding High-Level Features: An Approach To Robust Transfer...

引用

IEEE International Conference on Omni-Layer Intelligent Systems (IEEE COINS)

作者： Cheret, Laurent Yves Emile Ramos de Oliveira, Thiago Eustaquio Alves Lakehead Univ Dept Comp Sci Thunder Bay ON Canada

ISBN: (数字)9781665483568

ISBN: (纸本)9781665483568

Transfer Learning (TL) plays a vital role in image classification systems based on Deep Convolutional Neural Networks (DCNNs). Systems employing such technique may be susceptible to distortions on images, motivating the development of robust DCNNs capable of facing these problems. Unfortunately, changes in the architecture of DCNNs are sometimes specific to a kind of distortion and result in models that need to be retrained from scratch. This work proposes the use of autoencoders as intermediaries between pre-trained DCNNs and classifiers, delegating the denoising task to this architecture trained to encode feature maps. The classifiers are then trained to map the inputs from the autoencoder latent spaces to their respective classes. Models employing this approach achieved 3% to 4% increase in accuracy and 50% to 70% reduction in loss on the CIFAR10 and CIFAR100 datasets. The results also showed an up to 80% reduction in loss and up to 15% increase in accuracy for images with unseen distortions compared to the classical TL approach. This work improves classification results and increases robustness to distortions in a straightforward manner.

关键词： Image Classification variational autoencoder Transfer Learning Deep Convolutional Networks Impulse Noise Gaussian JPEG Compression

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A Deep Generative Recommendation Method for Unbiased Learning from Implicit Feedback 23

A Deep Generative Recommendation Method for Unbiased Learnin...

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13th ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR)

作者： Gupta, Shashank Oosterhuis, Harrie de Rijke, Maarten Univ Amsterdam Amsterdam Netherlands Radboud Univ Nijmegen Nijmegen Netherlands

ISBN: (纸本)9798400700736

variational autoencoders (VAEs) are the state-of-the-art model for recommendation with implicit feedback signals. Unfortunately, implicit feedback suffers from selection bias, e.g., popularity bias, position bias, etc., and as a result, training from such signals produces biased recommendation models. Existing methods for debiasing the learning process have not been applied in a generative setting. We address this gap by introducing an inverse propensity scoring (IPS) based method for training VAEs from implicit feedback data in an unbiased way. Our IPS-based estimator for the VAE training objective, VAE-IPS, is provably unbiased w.r.t. selection bias. Our experimental results show that the proposed VAE-IPS model reaches significantly higher performance than existing baselines. Our contributions enable practitioners to combine state-of-the-art VAE recommendation techniques with the advantages of bias mitigation for implicit feedback.

关键词： variational autoencoder Implicit feedback Selection bias

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Knowledge Extraction from XCSR Based on Dimensionality Reduction and Deep Generative Models

Knowledge Extraction from XCSR Based on Dimensionality Reduc...

引用

IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Tadokoro, Masakazu Hasegawa, Satoshi Tatsumi, Takato Sato, Hiroyuki Takadama, Keiki Univ Electrocommun Tokyo Japan

ISBN: (纸本)9781728121536

This paper proposes a novel learning classifier system (LCS) framework named ELSDeCS (Encoding, Learning, Sampling, and Decoding Classifier System) which can employ any dimensionality reduction method as pre-processing of learning and has its own components for extracting interpretable rule representations. We also propose two LCSs as examples of ELSDeCS. The first is DCAXCSR2, which is a revised version of the conventional system, and the second is VAEXCSR, which employs a deep generative model for dimensionality reduction. The experimental results on a classification task of handwritten digits show that only VAEXCSR can extract useful rule representations thanks to its robustness of decoding newly generated samples.

关键词： data mining XCS variational autoencoder interpretability

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