Search Results - We propose a multi-layer variational autoencoder method~

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

File Description: application/pdf

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

Building damage detection from multi-feature fusion of Sentinel-1/2 imagery using variational autoencoder and MLP-Mixer network: insights from the Jishishan earthquake

Authors: Jing Wang Xiwei Fan Yuanmeng Qi et al.

Source: Big Earth Data, Pp 1-40 (2025)

Subject Terms: Building damage detection, variational autoencoder, multi-layer perceptron, InSAR, earthquake, Geography. Anthropology. Recreation, Geology, QE1-996.5

File Description: electronic resource

Relation: https://doaj.org/toc/2096-4471; https://doaj.org/toc/2574-5417

Access URL: https://doaj.org/article/6015177fd5ab4390b318f6a700079ad8

Small sample pipeline DR defect detection based on smooth variational autoencoder and enhanced detection head faster RCNN.

Authors: Zhang, Ting You, Tianyang Liu, Zhaoying et al.

Source: Applied Intelligence; Jul2025, Vol. 55 Issue 10, p1-29, 29p

Subject Terms: AUTOENCODERS, SAMPLE size (Statistics), CLASSIFICATION, CALIBRATION

The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless Fingerprinting

Authors: Huang, Teng-Hui Dahanayaka, Thilini Thilakarathna, Kanchana et al.

Source: GLOBECOM 2023 - 2023 IEEE Global Communications Conference. :820-825

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Information Theory, Information Theory (cs.IT), 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2303.15860

Smoke and mirrors: A variational optuna autoencoder with reinforcement learning for intrusion detection in IoT networks.

Authors: Asante, Isaac Osei Wu, Libing

Source: Cluster Computing; 2025, Vol. 28 Issue 11, p1-20, 20p

Subject Terms: INTRUSION detection systems (Computer security), INTERNET of things, LATENT variables, MACHINE learning, REINFORCEMENT learning, INTERNET security, COMPUTER network security

Multi-Task Trajectory Prediction Using a Vehicle-Lane Disentangled Conditional Variational Autoencoder.

Authors: Chen, Haoyang Li, Na Shan, Hangguan et al.

Source: Sensors (14248220); Jul2025, Vol. 25 Issue 14, p4505, 22p

Subject Terms: AUTONOMOUS vehicles, PREDICTION models, MACHINE learning, PROBABILISTIC generative models

Unsupervised Anomaly Detection in Time Series Data via Enhanced VAE-Transformer Framework.

Authors: Zhang, Chunhao Xie, Bin Huo, Zhibin

Source: Computers, Materials & Continua; 2025, Vol. 84 Issue 1, p843-860, 18p

Subject Terms: LONG short-term memory, ANOMALY detection (Computer security), TIME series analysis, AUTOENCODERS, FEATURE extraction

Small-data image classification via drop-in variational autoencoder.

Authors: Mahdian, Babak Nedbal, Radim

Source: Signal, Image & Video Processing; Sep2025, Vol. 19 Issue 9, p1-9, 9p

Optimizing path reasoning scheme to improve conversational recommendation system.

Authors: Chen, Xinyu Huang, Xianying Liu, Fengjin et al.

Source: Evolving Systems; Dec2025, Vol. 16 Issue 4, p1-18, 18p

An Effective Method for Detecting Unknown Types of Attacks Based on Log-Cosh Variational Autoencoder.

Authors: Yu, Li Xu, Liuquan Jiang, Xuefeng

Source: Applied Sciences (2076-3417); Nov2023, Vol. 13 Issue 22, p12492, 21p

Subject Terms: FEATURE extraction, EXTRACTION techniques, DATA distribution, INTRUSION detection systems (Computer security)

Video Colorization Based on Variational Autoencoder.

Authors: Zhang, Guangzi Hong, Xiaolin Liu, Yan et al.

Source: Electronics (2079-9292); Jun2024, Vol. 13 Issue 12, p2412, 19p

Subject Terms: GRAYSCALE model, VIDEO processing, VIDEOS, VIDEO surveillance

MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models.

Authors: Wang, Feng Chu, Jinming Shen, Liyan et al.

Source: BMC Biology; 8/11/2025, Vol. 23 Issue 1, p1-24, 24p

Subject Terms: PROTEIN-protein interactions, DEEP learning, LANGUAGE models, GRAPH neural networks, BIOINFORMATICS, SACCHAROMYCES cerevisiae

Multimodal variational autoencoder for inverse problems in geophysics: application to a 1-D magnetotelluric problem.

Authors: Rodriguez, Oscar Taylor, Jamie M Pardo, David

Source: Geophysical Journal International; Dec2023, Vol. 235 Issue 3, p2598-2613, 16p

Subject Terms: INVERSION (Geophysics), GEOPHYSICS, INVERSE problems, MULTIMODAL user interfaces, DEEP learning

Diverse Image Captioning via Conditional Variational Autoencoder and Dual Contrastive Learning.

Authors: JING XU BING LIU YONG ZHOU et al.

Source: ACM Transactions on Multimedia Computing, Communications & Applications; Jan2024, Vol. 20 Issue 1, p1-16, 16p

Subject Terms: SEQUENTIAL learning, LEARNING, ENTROPY

Model Predictive Control with Variational Autoencoders for Signal Temporal Logic Specifications.

Authors: Im, Eunji Choi, Minji Cho, Kyunghoon

Source: Sensors (14248220); Jul2024, Vol. 24 Issue 14, p4567, 17p

Subject Terms: DIFFERENTIABLE dynamical systems, PREDICTION models, LOGIC, ITERATIVE learning control, SATISFACTION

Diffangle-Grasp: Dexterous Grasp Synthesis via Fine-Grained Contact Generation and Natural Pose Optimization.

Authors: Ning, Meng Deng, Chong Zhan, Ziheng et al.

Source: Biomimetics (2313-7673); Aug2025, Vol. 10 Issue 8, p492, 20p

Subject Terms: PREHENSION (Physiology), HUMANOID robots, LATENT variables, POSTURE, STOCHASTIC models

Disentangled Variational Autoencoder for Emotion Recognition in Conversations.

Authors: Yang, Kailai Zhang, Tianlin Ananiadou, Sophia

Source: IEEE Transactions on Affective Computing; Apr-Jun2024, Vol. 15 Issue 2, p508-518, 11p

Variational Autoencoders-Based Algorithm for Multi-Criteria Recommendation Systems.

Authors: Fraihat, Salam Shambour, Qusai Al-Betar, Mohammed Azmi et al.

Source: Algorithms; Dec2024, Vol. 17 Issue 12, p561, 15p

Subject Terms: NATURAL language processing, PATTERN recognition systems, RECOMMENDER systems, STANDARD deviations, IMAGE analysis

CVGAE: A Self-Supervised Generative Method for Gene Regulatory Network Inference Using Single-Cell RNA Sequencing Data.

Authors: Liu, Wei Teng, Zhijie Li, Zejun et al.

Source: Interdisciplinary Sciences: Computational Life Sciences; Dec2024, Vol. 16 Issue 4, p990-1004, 15p

Subject Terms: GRAPH neural networks, MULTILAYER perceptrons, GENE expression, RNA sequencing, GAUSSIAN distribution

Self-supervised variational autoencoder towards recommendation by nested contrastive learning.

Authors: Wang, Jing Wu, Jun Jia, Caiyan et al.

Source: Applied Intelligence; Aug2023, Vol. 53 Issue 15, p18887-18897, 11p

Subject Terms: DEEP learning, RECOMMENDER systems