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检索条件"主题词=Variational autoencoder"

共 1537 条记录，以下是1251-1260 订阅

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Future Network Traffic Matrix Synthesis and Estimation Based on Deep Generative Models 30

Future Network Traffic Matrix Synthesis and Estimation Based...

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30th International Conference on Computer Communications and Networks (ICCCN)

作者： Kakkavas, Grigorios Kalntis, Michail Karyotis, Vasileios Papavassiliou, Symeon Natl Tech Univ Athens Sch Elect & Comp Engn Iroon Polytech 9 Athens 15780 Greece Ionian Univ Dept Informat Tsirigoti Sq 7 Corfu 49132 Greece

ISBN: (纸本)9780738113302

Traffic matrices (TMs) contain information that is essential for network management, traffic engineering, and anomaly detection. However, constructing a TM through direct traffic measurements has a high administrative and computational cost. A more feasible approach is to estimate the TM from the easily obtainable link load measurements. In this paper, we address the issue of traffic matrix estimation (TME) from link loads using a deep generative model - namely, a variational autoencoder (VAE) - to solve the respective ill-posed inverse problem. In particular, we train the VAE with historical data (previously observed TMs) and we leverage the trained decoder to transform TME into a minimization problem in the latent space, which in turn can be solved by employing a gradient-based optimizer. Furthermore, the trained decoder can be used for traffic matrix synthesis, i.e., for generating synthetic TM examples that have "similar" properties to the samples of the training set. Finally, we explore the incremental optimization of the sequence of objectives constructed from the sequence of decoders that we obtain at different stages of the VAE training. The performance of the proposed methods is evaluated using a publicly available dataset of actual traffic matrices recorded in a real backbone network.

关键词： Network monitoring network tomography traffic matrix estimation deep learning variational autoencoder

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Investigation of using disentangled and interpretable representations for one-shot cross-lingual voice conversion 19

Investigation of using disentangled and interpretable repres...

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

作者： Mohammadi, Seyed Hamidreza Kim, Taehwan ObEN Inc Pasadena CA 91103 USA

ISBN: (纸本)9781510872219

We study the problem of cross-lingual voice conversion in non-parallel speech corpora and one-shot learning setting. Most prior work require either parallel speech corpora or enough amount of training data from a target speaker. However, we convert an arbitrary sentences of an arbitrary source speaker to target speaker's given only one target speaker training utterance. To achieve this, we formulate the problem as learning disentangled speaker-specific and context-specific representations and follow the idea of [1] which uses Factorized Hierarchical variational autoencoder (FHVAE). After training FHVAE on multi speaker training data, given arbitrary source and target speakers' utterance, we estimate those latent representations and then reconstruct the desired utterance of converted voice to that of target speaker. We investigate the effectiveness of the approach by conducting voice conversion experiments with varying size of training utterances and it was able to achieve reasonable performance with even just one training utterance. We also examine the speech representation and show that World vocoder outperforms Short-time Fourier Transform (STFT) used in [1]. Finally, in the subjective tests, for one language and cross-lingual voice conversion, our approach achieved significantly better or comparable results compared to VAE-STFT and GMM baselines in speech quality and similarity.

关键词： voice conversion one-shot learning cross lingual variational autoencoder

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A STUDY ON THE EFFECT OF COLOR SPACES IN LEARNED IMAGE COMPRESSION 31

A STUDY ON THE EFFECT OF COLOR SPACES IN LEARNED IMAGE COMPR...

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2024 International Conference on Image Processing

作者： Prativadibhayankaram, Srivatsa Panda, Mahadev Prasad Seiler, Juergen Richter, Thomas Sparenberg, Heiko Foessel, Siegfried Kaup, Andre Fraunhofer Inst Integrated Circuits IIS Moving Picture Technol Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc Erlangen Germany RheinMain Univ Appl Sci Media Concept & Prod Wiesbaden Germany

ISBN: (纸本)9798350349405;9798350349399

In this work, we present a comparison between color spaces namely YUV, LAB, RGB and their effect on learned image compression. For this we use the structure and color based learned image codec (SLIC) from our prior work, which consists of two branches - one for the luminance component (Y or L) and another for chrominance components (UV or AB). However, for the RGB variant we input all 3 channels in a single branch, similar to most learned image codecs operating in RGB. The models are trained for multiple bitrate configurations in each color space. We report the findings from our experiments by evaluating them on various datasets and compare the results to state-of-the-art image codecs. The YUV model performs better than the LAB variant in terms of MS-SSIM with a Bjontegaard delta bitrate (BD-BR) gain of 7.5% using VTM intra-coding mode as the baseline. Whereas the LAB variant has a better performance than YUV model in terms of CIEDE2000 having a BD-BR gain of 8%. Overall, the RGB variant of SLIC achieves the best performance with a BD-BR gain of 13.14% in terms of MS-SSIM and a gain of 17.96% in CIEDE2000 at the cost of a higher model complexity.

关键词： Deep learning learned image compression image compression variational autoencoder color learning color spaces

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Better Integrating Vision and Semantics for Improving Few-shot Classification 23

Better Integrating Vision and Semantics for Improving Few-sh...

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31st ACM International Conference on Multimedia (MM)

作者： Li, Zhuoling Wang, Yong Cent South Univ Changsha Hunan Peoples R China

ISBN: (纸本)9798400701085

Some recent methods address few-shot classification by integrating visual and semantic prototypes. However, they usually ignore the difference in feature structure between the visual and semantic modalities, which leads to limited performance improvements. In this paper, we propose a novel method, called bimodal integrator (BMI), to better integrate visual and semantic prototypes. In BMI, we first construct a latent space for each modality via a variational autoencoder, and then align the semantic latent space to the visual latent space. Through this semantics-to-vision alignment, the semantic modality is mapped to the visual latent space and has the same feature structure as the visual modality. As a result, the visual and semantic prototypes can be better integrated. In addition, based on the multivariate Gaussian distribution and the prompt engineering, a data augmentation scheme is designed to ensure the accuracy of modality alignment during the training process. Experimental results demonstrate that BMI significantly improves few-shot classification, making simple baselines outperform the most advanced methods on miniImageNet and tieredImageNet datasets.

关键词： modality alignment few-shot classification vision and semantics variational autoencoder

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Crickets, Cross-Veins, Crumpling, Crystals, and Computers

Crickets, Cross-Veins, Crumpling, Crystals, and Computers

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作者： Hoffmann, Jordan Harvard University

学位级别：Ph.D., Doctor of Philosophy

The world around us appears unimaginably complex: creases on sheets of paper, the patterns on animals, not to mention the assembly of life itself. In this thesis, I use computational tools to shed light on the organizing principles of a selection of systems that appear disordered at first glance. The topics are diverse, as are the tools I used to investigate them. Yet, in all cases, the ultimate aim has been the same: to uncover simple rules/patterns about seemingly complex systems using an evolving computational toolbox. The first chapter looks at how nuclei arrange themselves in the dance of life. In collaboration with the Extavour Lab (Harvard Department of Organismic and Evolutionary Biology (OEB)), we tracked live imaged 3-D datasets of nuclei from the cricket Gryllus bimaculatus. We found that nearly every quantifiable aspect of the motion of nuclei can be explained by the local density that the nuclei experience. From this experimental data, we developed a computational model that we used to bolster our findings and make concrete predictions about embryonic development. Some of these predictions we were able to experimentally validate through experimental modification of developing embryos. In the second chapter, my collaborators and I characterize the geometric patterns formed by the veins in insect wings. Dividing up the wing into a series of polygonal shapes, we ask geometric questions about the open spaces formed by the veins. Looking at odonate wings (dragonflies and damselflies), we propose a simple developmental that is able to recapitulate the complex patterns observed. Then, we extend the mathematical toolkit introduced in the first manuscript to a broader selection of insect wings. In the third chapter, I use machine learning to ask if we can uncover geometric order in a classically disordered system: crumpled sheets. We find that by augmenting experimental datasets of crumpled mylar with simulated examples from a sister system–rigid flat folding–we

关键词： Biology Computation Machine learning Physics Gryllus bimaculatus variational autoencoder

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Brain2Image: Converting Brain Signals into Images 17

<i>Brain2Image</i>: Converting Brain Signals into Images

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25th ACM International Conference on Multimedia (MM)

作者： Kavasidis, Isaak Palazzo, Simone Spampinato, Concetto Giordano, Daniela Shah, Mubarak Univ Catania PeRCeiVe Lab Via Santa Sofia 102 I-95127 Catania Italy Univ Cent Florida Ctr Res Comp Vis 4328 Scorpius St Orlando FL 32816 USA

ISBN: (纸本)9781450349062

Reading the human mind has been a hot topic in the last decades, and recent research in neuroscience has found evidence on the possibility of decoding, from neuroimaging data, how the human brain works. At the same time, the recent rediscovery of deep learning combined to the large interest of scientific community on generative methods has enabled the generation of realistic images by learning a data distribution from noise. The quality of generated images increases when the input data conveys information on visual content of images. Leveraging on these recent trends, in this paper we present an approach for generating images using visually-evoked brain signals recorded through an electroencephalograph (EEG). More specifically, we recorded EEG data from several subjects while observing images on a screen and tried to regenerate the seen images. To achieve this goal, we developed a deep-learning framework consisting of an LSTM stacked with a generative method, which learns a more compact and noise-free representation of EEG data and employs it to generate the visual stimuli evoking specific brain responses. Our Brain2Image approach was trained and tested using EEG data from six subjects while they were looking at images from 40 ImageNet classes. As generative models, we compared variational autoencoders (VAE) and generative adversarial networks (GAN). The results show that, indeed, our approach is able to generate an image drawn from the same distribution of the shown images. Furthermore, GAN, despite generating less realistic images, show better performance than VAE, especially as concern sharpness. The obtained performance provides useful hints on the fact that EEG contains patterns related to visual content and that such patterns can be used to effectively generate images that are semantically coherent to the evoking visual stimuli.

关键词： variational autoencoder EEG image generation

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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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A Radar HRRP Target Recognition Method Based on Conditional Wasserstein VAEGAN and 1-D CNN 5th

A Radar HRRP Target Recognition Method Based on Conditional ...

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5th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： He, Jiaxing Wang, Xiaodan Xiang, Qian Air Force Engn Univ Xian Shaanxi Peoples R China

ISBN: (纸本)9783031189067;9783031189074

Radar high resolution range profile (HRRP) contains important structural features such as target size and scattering center distribution, which has attracted extensive attention in the field of radar target recognition. Aiming at the problems of class imbalance and insufficient data quality generated by vanilla GAN, an HRRP target recognition method based on improved conditional Wasserstein variational autoencoder Generative Adversarial Networks (CWVAEGAN) and one-dimensional convolutional neural network (CWVAEGAN-1DCNN) is proposed. CWVAEGAN class-balancing method introduces the concept of variational lower bound in VAE into GAN, and uses the encoded hidden vector instead of Gaussian noise as generator's input, which can significantly improve the fidelity of the generated samples and balance the class distribution of the dataset. The balanced dataset is used for classification through a standard 1D-CNN. The experimental results show that the samples generated by CWVAEGAN class-balancing method greatly improve the detection accuracy of 1D-CNN for minority classes, the fidelity of the samples generated by CWVAEGAN is higher than that of other class-balancing methods, and CWVAEGAN-1DCNN has strong HRRP target recognition ability.

关键词： High resolution range profile Class-balancing method Generative adversarial network variational autoencoder One-dimensional convolution neural network

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Few Shot Hematopoietic Cell Classification 6

Few Shot Hematopoietic Cell Classification

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6th International Conference on Medical Imaging with Deep Learning (MIDL)

作者： Nguyen, Vu Howlader, Prantik Hou, Le Samaras, Dimitris Gupta, Rajarsi Saltz, Joel SUNY Stony Brook Dept Comp Sci Stony Brook NY USA SUNY Stony Brook Dept Biomed Informat Stony Brook NY USA

We propose a few shot learning approach for the problem of hematopoietic cell classification in digital pathology. In hematopoiesis cell classification, the classes correspond to the different stages of the cellular maturation process. Two consecutive stage categories are considered to have a neighborhood relationship, which implies a visual similarity between the two categories. We propose RelationVAE which incorporates these relationships between hematopoietic cell classes to robustly generate more data for the classes with limited training data. Specifically, we first model these relationships using a graphical model, and propose RelationVAE, a deep generative model which implements the graphical model. RelationVAE is trained to optimize the lower bound of the pairwise data likelihood of the graphical model. In this way, it can identify class level features of a specific class from a small number of input images together with the knowledge transferred from visually similar classes, leading to more robust sample synthesis. The experiments on our collected hematopoietic dataset show the improved results of our proposed RelationVAE over a baseline VAE model and other few shot learning methods. Our code and data are available at https://***/cvlab-stonybrook/hematopoiesis-relationvae.

关键词： Hematopoiesis cell classification Few Shot Learning variational autoencoder

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Protecting gender and identity with disentangled speech representations 22

Protecting gender and identity with disentangled speech repr...

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Interspeech Conference

作者： Stoidis, Dimitrios Cavallaro, Andrea Queen Mary Univ London Ctr Intelligent Sensing London England

ISBN: (纸本)9781713836902

Besides its linguistic content, our speech is rich in biometric information that can be inferred by classifiers. Learning privacy-preserving representations for speech signals enables downstream tasks without sharing unnecessary, private information about an individual. In this paper, we show that protecting gender information in speech is more effective than modelling speaker-identity information only when generating a nonsensitive representation of speech. Our method relies on reconstructing speech by decoding linguistic content along with gender information using a variational autoencoder. Specifically, we exploit disentangled representation learning to encode information about different attributes into separate subspaces that can be factorised independently. We present a novel way to encode gender information and disentangle two sensitive biometric identifiers, namely gender and identity, in a privacyprotecting setting. Experiments on the LibriSpeech dataset show that gender recognition and speaker verification can be reduced to a random guess, protecting against classification-based attacks.

关键词： privacy soft biometrics disentangled representation learning variational autoencoder

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