Structural Damage Detection through Dual-Channel Pseudo-Supervised Learning Structural damage detection is crucial for maintaining the health and safety of buildings. However, achieving high accuracy in damage detection remains cha... T Hu,K Ma,J Xiao - Applied Sciences (2076-3417) 被引量: 0发表...
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(CIKM'2022) MIC: Model-agnostic Integrated Cross-channel Recommender [paper] (CIKM'2022) Temporal Contrastive Pre-Training for Sequential Recommendation [paper] (ICDE'2022) Contrastive Learning for Sequential Recommendation [paper] (ICDE'2022) Self-Supervised Dual-Channel Attentive Network for Session-...
To prevent the model from losing cross-channel information during training, we introduce the spectral gradient regularization ϕSPEC as a loss component in PA-CNN: $$\begin{array}{l}{L}_{\mathrm{SPEC}}=\phi _{\mathrm{SPEC}}=\frac{1}{{WH}\left(C-1\right)}\mathop{\sum }\limits_...
Conversely, larger models equipped with advanced mechanisms, such as transformers for leveraging long-range dependencies (ReconFormer) and channel-wise attention (MICCAN) for improved reconstructions, already perform well even with less input information. As a result, our contrastive learning framework ...
Luo et al.35 proposed UC-DenseNet, which combines CNN and RNN along with an improved attention mechanism to emphasize feature information through cross-channel communication. All these works require image-label pairs and do not leverage the large unlabeled endoscopic dataset that is available. We ...
longer than 4 min, indicating that there is a less than 1% likelihood that the clustered pattern of CCPs’ nucleation could be the result of random occurrence.dHistogram of mean square displacement (MSD) of 3572 CCP tracks from 3 cells.eDual-color time-lapse imaging of CCPs (green) and F...
Li et al. [14] used self-supervised dual-track learning to rank. Since there are more available COVID-19 negative samples than COVID-19 positive samples, their method selected a subset of the negative samples to train on the network so that a more balanced data was trained. The way the...
Dual-Channel Latent Factor Analysis Enhanced Graph Contrastive Learning for Recommendation (Graph + DA + CL) arXiv 2024, [PDF] Meta-optimized Structural and Semantic Contrastive Learning for Graph Collaborative Filtering (Graph + DA + CL) ICDE 2024, [PDF], [Code] Unveiling Vulnerabilities of Cont...
A dual-channel hypergraph structure is created based on these relationships. Then, the hypergraph convolution is applied to model the high-order interactions between users and items. Additionally, we adopted a self-supervised learning task to maximize the consistency between different views. It helps ...