Suchergebnisse - vector quantized variational autoencoder

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

Autoren: Mohammad Diqi Ema Utami Kusrini Kusrini et al.

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

Rapid Object Modeling Initialization for Vector Quantized-Variational AutoEncoder

Autoren: Michael Karnes Alper Yilmaz

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

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

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

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

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

Zugangs-URL: http://arxiv.org/abs/2409.16011

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

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

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

Schlagwörter: 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

Dateibeschreibung: application/pdf

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

Attention-Guided Vector Quantized Variational Autoencoder for Brain Tumor Segmentation

Autoren: Danish Ali Ajmal Mian Naveed Akhtar et al.

Quelle: Lecture Notes in Computer Science ISBN: 9783032049261

Quaternion Vector Quantized Variational Autoencoder

Autoren: Hui Luo Xin Liu Jian Sun et al.

Quelle: 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

Autoren: Zuyan Zhang Jingyan Han Hou-Bin Huan

Quelle: Journal of Circuits, Systems and Computers.

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

Autoren: Yue Zou Zhenhao Li Menghan Zhang et al.

Quelle: Medical Physics. 52

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

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

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

Schlagwörter: 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)

Zugangs-URL: http://arxiv.org/abs/2410.18283

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

Autoren: Yiyong Xiao Kai Shu Haoyi Zhang et al.

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

Decomposed Vector-Quantized Variational Autoencoder for Human Grasp Generation

Autoren: Zhe Zhao Mengshi Qi Huadong Ma

Quelle: Lecture Notes in Computer Science ISBN: 9783031733963

Schlagwörter: Computer Science - Computer Vision and Pattern Recognition

Zugangs-URL: http://arxiv.org/abs/2407.14062

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

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

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

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

Schlagwörter: 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

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2307.06720

Vector-Quantized Variational AutoEncoder for pansharpening

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

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

A Vector Quantized Masked Autoencoder for Speech Emotion Recognition

Autoren: Sadok, Samir Leglaive, Simon Séguier, Renaud

Weitere Verfasser: Sadok, Samir Leglaive, Simon Séguier, Renaud

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

Schlagwörter: 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

Dateibeschreibung: application/pdf

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

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

Autoren: Qien Yu Shengxin Dai Ran Dong et al.

Quelle: Pattern Recognition. 164:111500

Modeling Wound Healing Using Vector Quantized Variational Autoencoders and Transformers

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

Weitere Verfasser: Backová, Lenka Bengoetxea, Guillermo Rogalla, Svana et al.

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

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

Zugangs-URL: http://hdl.handle.net/10261/352620

Leveraging Vector-Quantized Variational Autoencoder Inner Metrics for Anomaly Detection

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

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

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

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

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

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

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

Schlagwörter: 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

Dateibeschreibung: application/pdf

Zugangs-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

FactorVQVAE: Discrete latent factor model via Vector Quantized Variational Autoencoder

Autoren: Namhyoung Kim Seung Eun Ock Jae Wook Song

Quelle: Knowledge-Based Systems. 318:113460

Generating High-Quality F0 Embeddings Using the Vector-Quantized Variational Autoencoder

Autoren: David Porteš Aleš Horák

Quelle: Lecture Notes in Computer Science ISBN: 9783031705656