Search Results - Quantized autoencoder

Quantized autoencoder (QAE) intrusion detection system for anomaly detection in resource-constrained IoT devices using RT-IoT2022 dataset by Sharmila, B S, Nagapadma, Rohini

“… This study proposes quantized autoencoder (QAE) model for intrusion detection systems to detect anomalies…”
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Quaternion Vector Quantized Variational Autoencoder by Luo, Hui, Liu, Xin, Sun, Jian, Zhang, Yang

“…Vector quantized variational autoencoders, as variants of variational autoencoders, effectively capture discrete representations by quantizing continuous latent spaces and are widely used in generative tasks…”
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Vector-Quantized Variational AutoEncoder for pansharpening by Talbi, Farid, Chikr Elmezouar, Miloud, Boutellaa, Elhocine, Alim, Fatiha

“… This paper describes a new, efficient, and accurate pansharpening architecture. The Vector-Quantized Variational AutoEncoder (VQ-VAE…”
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Variable Rate Allocation for Vector-Quantized Autoencoders by Baldassarre, Federico, El-Nouby, Alaaeldin, Jegou, Herve

“…Vector-quantized autoencoders have recently gained interest in image compression, generation and self-supervised learning…”
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A vector quantized masked autoencoder for audiovisual speech emotion recognition by Sadok, Samir, Leglaive, Simon, Séguier, Renaud

“… The model includes vector quantized variational autoencoders that compress raw audio and visual speech data into discrete tokens…”
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Resource‐Efficient Anomaly Detection in Industrial Control Systems With Quantized Recurrent Variational Autoencoder by Fährmann, Daniel, Ihlefeld, Malte, Kuijper, Arjan, Damer, Naser

“…), specifically tailored for resource‐constrained environments. At its core, the quantized gated recurrent unit variational autoencoder (Q‐GRU‐VAE…”
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Tackling the Nonlinearity Problem in Inverse Modeling: Mixture Density Network-Backed Quantized AutoEncoder by Talha Kilic, Muhammed Nur, Mao, Yuwei, Gupta, Vishu, Choudhary, Alok, Liao, Wei-keng, Agrawal, Ankit

“… In this work, we propose a novel Mixture Density Network (MDN) based Quan-tized Autoencoder Network designed to generate microstructure images from only a single data point…”
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Disentanglement with factor quantized variational autoencoders by Baykal, Gulcin, Kandemir, Melih, Unal, Gozde

“… In our work, we propose a discrete variational autoencoder (VAE) based model where the ground truth information about the generative factors not provided to the model…”
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FactorVQVAE: Discrete latent factor model via Vector Quantized Variational Autoencoder by Kim, Namhyoung, Ock, Seung Eun, Song, Jae Wook

“…This study introduces FactorVQVAE, the first integration of the Vector Quantized Variational Autoencoder (VQVAE…”
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Attention-based vector quantized variational autoencoder for anomaly detection by using orthogonal subspace constraints by Yu, Qien, Dai, Shengxin, Dong, Ran, Ikuno, Soichiro

“…This paper introduces a new framework that uses a vector quantized variational autoencoder (VQVAE…”
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Semantic-oriented learning-based image compression by Only-Train-Once quantized autoencoders by Sebai, D., Shah, A. Ulah

“…Accessibility to big training datasets together with current advances in computing power has emerged interest in the leverage of deep learning to address image…”
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Predicting anatomical variations in radiotherapy with a vector quantized variational autoencoder generative model by Zou, Yue, Li, Zhenhao, Zhang, Menghan, Li, Ziwei, Yin, Xiaojie, Yang, Long, Hu, Weigang, Wang, Jiazhou

“… Predicting these changes may benefit adaptive radiotherapy (ART) in nasopharyngeal cancer. Purpose This study proposes a vector quantized variational autoencoder (VQ‐VAE…”
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Quantized Generative Autoencoder for Audio Spectrograms by Cebedio, M. Celeste, Rabioglio, Lucas A., Micco, Luciana De

“… As a case study, we present a quantized generative autoencoder optimized for efficiently generating underwater sound spectrograms…”
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Frequency enhanced vector quantized variational autoencoder for structural vibration response compression by Xue, Zhilin, An, Yonghui, Ou, Jinping

“…•The frequency enhanced vector quantized variational autoencoder (FEVQVAE) is proposed for structural vibration response compression…”
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Enhancing into the Codec: Noise Robust Speech Coding with Vector-Quantized Autoencoders by Casebeer, Jonah, Vale, Vinjai, Isik, Umut, Valin, Jean-Marc, Giri, Ritwik, Krishnaswamy, Arvindh

“…Audio codecs based on discretized neural autoencoders have recently been developed and shown to provide significantly higher compression levels for comparable quality speech out-put…”
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Data augmentation for Gram-stain images based on Vector Quantized Variational AutoEncoder by V, Shwetha, Prasad, Keerthana, Mukhopadhyay, Chiranjay, Banerjee, Barnini

“… In this regard, we investigate a novel application of the Variational AutoEncoder. Specifically, the Vector Quantized Variational AutoEncoder model is trained to generate the Gram-stain images…”
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Avoiding Domain Drift and Constant Predictions with Diffusion Enhanced Vector-Quantized Autoencoders for Temperature Predictions by Lampl, Nina, de Freitas, Joao Machado, Fuchs, Alexander, Brezina, Benedikt, Klitzsch, Michael, Pernkopf, Franz

“… Using a vector-quantized autoencoder can mitigate the problem as it can map predictions back to the source domain, but it leads to almost constant predictions…”
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S-HR-VQVAE: Sequential Hierarchical Residual Learning Vector Quantized Variational Autoencoder for Video Prediction by Adiban, Mohammad, Stefanov, Kalin, Siniscalchi, Sabato Marco, Salvi, Giampiero

“… (i) a novel hierarchical residual learning vector quantized variational autoencoder (HR-VQVAE), and (ii) a novel autoregressive spatiotemporal predictive model…”
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A Vector Quantized Variational Autoencoder (VQ-VAE) Autoregressive Neural F0 Model for Statistical Parametric Speech Synthesis by Wang, Xin, Takaki, Shinji, Yamagishi, Junichi, King, Simon, Tokuda, Keiichi

“… In subjective evaluations, a deep AR model (DAR) outperformed an RNN. Here, we propose a Vector Quantized Variational Autoencoder (VQ-VAE…”
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SSL-VQ: vector-quantized variational autoencoders for semi-supervised prediction of therapeutic targets across diverse diseases by Namba, Satoko, Li, Chen, Yuyama Otani, Noriko, Yamanishi, Yoshihiro

“… rare diseases, intractable diseases). Results This study presents a novel machine learning approach using multimodal vector-quantized variational autoencoders (VQ-VAEs…”
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