Search Results - vector-quantized variational autoencoder

Leveraging Vector Quantized Variational Autoencoder for Accurate Synthetic Data Generation in Multivariate Time Series

Authors: Mohammad Diqi Ema Utami Kusrini Kusrini et al.

Source: MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer. 24:423-438

Rapid Object Modeling Initialization for Vector Quantized-Variational AutoEncoder

Authors: Michael Karnes Alper Yilmaz

Source: 2025 IEEE International Conference on Image Processing (ICIP). :2402-2407

CrowdSurfer: Sampling Optimization Augmented with Vector-Quantized Variational AutoEncoder for Dense Crowd Navigation

Authors: Kumar, Naman Singha, Antareep Nanwani, Laksh et al.

Source: 2025 IEEE International Conference on Robotics and Automation (ICRA). :16854-16860

Subject Terms: FOS: Computer and information sciences, Computer Science - Robotics, Optimization and Control (math.OC), FOS: Mathematics, Mathematics - Optimization and Control, Robotics (cs.RO)

Access URL: http://arxiv.org/abs/2409.16011

S-HR-VQVAE: Sequential Hierarchical Residual Learning Vector Quantized Variational Autoencoder for Video Prediction

Authors: Mohammad Adiban Kalin Stefanov Sabato Marco Siniscalchi et al.

Source: IEEE Transactions on Multimedia. 27:4321-4332

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, I.2.10, I.4.2, Computer Science - Artificial Intelligence, I.2.6, I.4.5, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, I.4.10, Machine Learning (cs.LG), Hierarchical Modeling, Video Prediction, Artificial Intelligence (cs.AI), Autoregressive Modeling, Settore IINF-05/A - Sistemi di elaborazione delle informazioni

File Description: application/pdf

Access URL: http://arxiv.org/abs/2307.06701
https://hdl.handle.net/10447/672963

Attention-Guided Vector Quantized Variational Autoencoder for Brain Tumor Segmentation

Authors: Danish Ali Ajmal Mian Naveed Akhtar et al.

Source: Lecture Notes in Computer Science ISBN: 9783032049261

Quaternion Vector Quantized Variational Autoencoder

Authors: Hui Luo Xin Liu Jian Sun et al.

Source: IEEE Signal Processing Letters. 32:151-155

Generation of Ocular Anatomy and Disease Images Using Vector Quantized Variational Autoencoder for Image Quality Improvement and Feature Extraction

Authors: Zuyan Zhang Jingyan Han Hou-Bin Huan

Source: Journal of Circuits, Systems and Computers.

Predicting anatomical variations in radiotherapy with a vector quantized variational autoencoder generative model

Authors: Yue Zou Zhenhao Li Menghan Zhang et al.

Source: Medical Physics. 52

Augmenting Training Data with Vector-Quantized Variational Autoencoder for Classifying RF Signals

Authors: Kompella, Srihari Kamesh Davaslioglu, Kemal Sagduyu, Yalin E. et al.

Source: MILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM). :1-6

Subject Terms: Computer Science - Networking and Internet Architecture, Networking and Internet Architecture (cs.NI), Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2410.18283

EGGesture: Entropy-Guided Vector Quantized Variational AutoEncoder for Co-Speech Gesture Generation

Authors: Yiyong Xiao Kai Shu Haoyi Zhang et al.

Source: Proceedings of the 32nd ACM International Conference on Multimedia. :6113-6122

Decomposed Vector-Quantized Variational Autoencoder for Human Grasp Generation

Authors: Zhe Zhao Mengshi Qi Huadong Ma

Source: Lecture Notes in Computer Science ISBN: 9783031733963

Subject Terms: Computer Science - Computer Vision and Pattern Recognition

Access URL: http://arxiv.org/abs/2407.14062

Weakly Supervised Marine Animal Detection from Remote Sensing Images Using Vector-Quantized Variational Autoencoder

Authors: Pham, Minh-Tan Gangloff, Hugo Lefèvre, Sébastien et al.

Contributors: Pham, Minh-Tan Gangloff, Hugo Lefèvre, Sébastien et al.

Source: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. :5559-5562

Subject Terms: FOS: Computer and information sciences, aerial remote sensing, weakly-supervised learning, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 14. Life underwater, Anomaly detection, marine animal monitoring, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing

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Access URL: http://arxiv.org/abs/2307.06720

Vector-Quantized Variational AutoEncoder for pansharpening

Authors: Farid Talbi Miloud Chikr Elmezouar Elhocine Boutellaa et al.

Source: International Journal of Remote Sensing. 44:6329-6349

Attention-based vector quantized variational autoencoder for anomaly detection by using orthogonal subspace constraints

Authors: Qien Yu Shengxin Dai Ran Dong et al.

Source: Pattern Recognition. 164:111500

Modeling Wound Healing Using Vector Quantized Variational Autoencoders and Transformers

Authors: Backová, Lenka Bengoetxea, Guillermo Rogalla, Svana et al.

Contributors: Backová, Lenka Bengoetxea, Guillermo Rogalla, Svana et al.

Source: Digital.CSIC. Repositorio Institucional del CSIC
Consejo Superior de Investigaciones Científicas (CSIC)

Subject Terms: Video prediction, Wound healing, Deep learning, 3. Good health

Access URL: http://hdl.handle.net/10261/352620

Leveraging Vector-Quantized Variational Autoencoder Inner Metrics for Anomaly Detection

Authors: Gangloff, Hugo Pham, Minh-Tan Courtrai, Luc et al.

Contributors: Gangloff, Hugo Pham, Minh-Tan Courtrai, Luc et al.

Source: 2022 26th International Conference on Pattern Recognition (ICPR). :435-441

Subject Terms: [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://hal.science/hal-03991178v1
https://doi.org/10.1109/icpr56361.2022.9956102

A Vector Quantized Masked Autoencoder for Speech Emotion Recognition

Authors: Sadok, Samir Leglaive, Simon Séguier, Renaud

Contributors: Sadok, Samir Leglaive, Simon Séguier, Renaud

Source: 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). :1-5

Subject Terms: Self-supervised learning, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, Computer Science - Machine Learning, Sound (cs.SD), masked autoencoder, [INFO.INFO-SD] Computer Science [cs]/Sound [cs.SD], [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Computer Science - Sound, Machine Learning (cs.LG), vector-quantized variational autoencoder, speech emotion recognition, Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Electrical Engineering and Systems Science - Audio and Speech Processing

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Access URL: http://arxiv.org/abs/2304.11117
https://hal.science/hal-04080024v1/document
https://hal.science/hal-04080024v1

Hierarchical Residual Learning Based Vector Quantized Variational Autoencoder for Image Reconstruction and Generation

Authors: Adiban M. Stefanov K. Siniscalchi S. M. et al.

Subject Terms: Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni, I.2, FOS: Computer and information sciences, Computer Science - Machine Learning, I.4, Computer Sciences, namely VQVAE and VQVAE-2. HR-VQVAE can also generate high-quality and diverse images that outper- form state-of-the-art generative models, Computer Vision and Pattern Recognition (cs.CV), the representations at each layer are hierarchically linked to those at previous layers. We evaluate our method on the tasks of image reconstruction and generation. Experimental results demonstrate that the discrete representations learned by HR-VQVAE enable the decoder to reconstruct high-quality images with less distortion than the baseline methods, Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, each layer in HR-VQVAE learns a discrete representation of the residual from previous layers through a vector quantized encoder. Furthermore, We propose a multi-layer variational autoencoder method, we call HR-VQVAE, that learns hierarchical discrete representations of the data. By utilizing a novel objective function, each layer in HR-VQVAE learns a discrete representation of the residual from previous layers through a vector quantized encoder. Furthermore, the representations at each layer are hierarchically linked to those at previous layers. We evaluate our method on the tasks of image reconstruction and generation. Experimental results demonstrate that the discrete representations learned by HR-VQVAE enable the decoder to reconstruct high-quality images with less distortion than the baseline methods, namely VQVAE and VQVAE-2. HR-VQVAE can also generate high-quality and diverse images that outper- form state-of-the-art generative models, providing further verification of the efficiency of the learned representations. The hierarchical nature of HR-VQVAE i) reduces the decoing search time, making the method particularly suitable for high-load tasks and ii) allows to increase the codebook size without incurring the codebook collapse problem, that learns hierarchical discrete representations of the data. By utilizing a novel objective function, Machine Learning (cs.LG), 03 medical and health sciences, Datavetenskap (datalogi), 0302 clinical medicine, We propose a multi-layer variational autoencoder method, providing further verification of the efficiency of the learned representations. The hierarchical nature of HR-VQVAE i) reduces the decoing search time, making the method particularly suitable for high-load tasks and ii) allows to increase the codebook size without incurring the codebook collapse problem, 0202 electrical engineering, electronic engineering, information engineering, we call HR-VQVAE

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Access URL: http://arxiv.org/abs/2208.04554
https://hdl.handle.net/10447/637533
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-324770

STORM: A Spatio-Temporal Factor Model Based on Dual Vector Quantized Variational Autoencoders for Financial Trading

Authors: Zhao, Yilei Zhang, Wentao Yang, Tingran et al.

Index Terms: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, text

URL: http://arxiv.org/abs/2412.09468

FactorVQVAE: Discrete latent factor model via Vector Quantized Variational Autoencoder

Authors: Namhyoung Kim Seung Eun Ock Jae Wook Song

Source: Knowledge-Based Systems. 318:113460