Suchergebnisse - commonality autoencoder

Commonality Autoencoder: Learning Common Features for Change Detection From Heterogeneous Images von Wu, Yue, Li, Jiaheng, Yuan, Yongzhe, Qin, A. K., Miao, Qi-Guang, Gong, Mao-Guo

“… ) for feature extraction and the commonality autoencoder for commonalities exploration. The CAE can eliminate a large part of redundancies in two heterogeneous images and obtain more consistent feature representations …”
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AEKAN: Exploring Superpixel-Based AutoEncoder Kolmogorov-Arnold Network for Unsupervised Multimodal Change Detection von Liu, Tongfei, Xu, Jianjian, Lei, Tao, Wang, Yingbo, Du, Xiaogang, Zhang, Weichuan, Lv, Zhiyong, Gong, Maoguo

“… ), making it difficult to extract change information. To overcome this challenge, we propose a novel superpixel-based AutoEncoder Kolmogorov-Arnold Network (AEKAN …”
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Deep contrastive multi-view clustering with doubly enhanced commonality von Yang, Zhiyuan, Zhu, Changming, Li, Zishi

“… Recently, deep multi-view clustering leveraging autoencoders has garnered significant attention due to its ability to simultaneously enhance feature learning capabilities and optimize clustering outcomes …”
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Multitask Shape Optimization Using a 3-D Point Cloud Autoencoder as Unified Representation von Rios, Thiago, van Stein, Bas, Back, Thomas, Sendhoff, Bernhard, Menzel, Stefan

“… The choice of design representations, as of search operators, is central to the performance of evolutionary optimization algorithms, in particular, for …”
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SwinDAE: Electrocardiogram Quality Assessment Using 1D Swin Transformer and Denoising AutoEncoder von Chen, Guanyu, Shi, Tianyi, Xie, Baoxing, Zhao, Zhicheng, Meng, Zhu, Huang, Yadong, Dong, Jin

“… : In this paper, an effective model named Swin Denoising AutoEncoder (SwinDAE) is proposed. Specifically, SwinDAE uses a DAE as the basic architecture, and incorporates a 1D Swin Transformer during the feature learning stage of the encoder and decoder …”
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Graph Representation Learning Beyond Node and Homophily von Li, You, Lin, Bei, Luo, Binli, Gui, Ning

“… Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding. However, existing …”
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Multifractal-Aware Convolutional Attention Synergistic Network for Carbon Market Price Forecasting von Wei, Liran, Tang, Mingzhu, Li, Na, Deng, Jingwen, Zhou, Xinpeng, Hu, Haijun

“… Accurate carbon market price prediction is crucial for promoting a low-carbon economy and sustainable engineering. Traditional models often face challenges in …”
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Commonality Feature Representation Learning for Unsupervised Multimodal Change Detection von Liu, Tongfei, Zhang, Mingyang, Gong, Maoguo, Zhang, Qingfu, Jiang, Fenlong, Zheng, Hanhong, Lu, Di

“… ) cannot be compared directly to identify changes. To overcome this problem, this paper proposes a novel commonality feature representation learning (CFRL …”
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A cross-linguistic depression detection method based on speech data von Qin, Shengjie, Zhang, Yuezhou, Ma, Yuliang, Li, Hui, Li, Xingxing, Lian, Bin, Cai, Weiming, Cui, Jialin, Zhao, Xianghong

“… We used down-sampled speech data (1 kHz), features extracted by a Convolutional AutoEncoder, and manually selected features to explore commonalities across languages and compare our method with other models …”
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A Generalized Few-Shot Object Detection Method via Extraction of Base-Novel Commonality With Memory Distillation of Category Prototypes von Su, Junchi, Gao, Xin, Lu, Heping, Li, Baofeng, Zhai, Feng, Fang, Xiao, Wang, Taizhi, Li, Qiangwei

“… ). The variational prototype refinement module introduces a class-agnostic feature fusion mechanism based on the original variational autoencoder …”
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Merging conformational landscapes in a single consensus space with FlexConsensus algorithm von Herreros, David, Perez Mata, Carlos, Sanchez Sorzano, Carlos Oscar, Carazo, Jose Maria

“… : a multi-autoencoder neural network able to learn the commonalities and differences among several conformational …”
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Disentangle the group and individual components of functional connectome with autoencoders von Pei, Zhaodi, Zhu, Zhiyuan, Zhen, Zonglei, Wu, Xia

“… One of the central goals of neuroscience is to understand the group commonality and individual variability in functional connectome …”
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Hybrid domain adaptation for sensor-based human activity recognition in a heterogeneous setup with feature commonalities von Prabono, Aria Ghora, Yahya, Bernardo Nugroho, Lee, Seok-Lyong

“… Common approaches in the cross-domain sensor-based human activity recognition are based on the homogeneous domain adaptation which relies on the assumption …”
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An Integrated Multitasking Intelligent Bearing Fault Diagnosis Scheme Based on Representation Learning Under Imbalanced Sample Condition von Zhang, Jiusi, Zhang, Ke, An, Yiyao, Luo, Hao, Yin, Shen

“… Furthermore, there are commonalities between the bearing fault detection, classification, and identification tasks …”
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Instance-aware diversity feature generation for unsupervised person re-identification von Zhang, Xiaowei, Dou, Xiao, Zhao, Xinpeng, Li, Guocong, Wang, Zekang

“… However, the previous approaches including cluster-level or instance-level contrast loss, did not fully explore inherent commonality of each identified individual from unlabeled samples …”
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Unsupervised Graph Representation Learning Beyond Aggregated View von Zhou, Jian, Li, Jiasheng, Kuang, Li, Gui, Ning

“… To address this issue, this paper proposes a novel Graph Dual-view AutoEncoder framework (GDAE) which introduces the node-wise view for an individual node beyond the traditional aggregated view for aggregation of connected nodes …”
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Representation Learning via Semi-Supervised Autoencoder for Multi-task Learning von Zhuang, Fuzhen, Luo, Dan, Jin, Xin, Xiong, Hui, Luo, Ping, He, Qing

“… Multi-task learning aims at learning multiple related but different tasks. In general, there are two ways for multi-task learning. One is to exploit the small …”
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Self-Supervised Deep Multiview Spectral Clustering von Zong, Linlin, Miao, Faqiang, Zhang, Xianchao, Liang, Wenxin, Xu, Bo

“… of automatically retrieved pairwise constraints. First, the fused multiple autoencoders are used to extract the latent consistent feature of multiple views …”
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Mode Information Separated β-VAE Regression for Multimode Industrial Process Soft Sensing von Shen, Bingbing, Yao, Le, Yang, Zeyu, Ge, Zhiqiang

“… Since different data modes are derived from the same reaction process, certain commonalities that represent the substantial characteristics of the process could exist …”
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scCMP: A Deep Learning Method for Identifying Clonal Mutational Profiles From Single-Cell Genomic Data von Zhou, Junlei, Li, Ruixiang, Shi, Fangyuan, Huo, Xianhao, Du, Fang, Yu, Zhenhua

“… Accurately inferring clonal mutational profiles is essential for understanding intra-tumor heterogeneity and clonal selection during tumor evolution …”
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