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Found 8 papers, 2 papers with code
3D-EffiViTCaps: 3D Efficient Vision Transformer with Capsule for Medical Image Segmentation
Our encoder uses capsule blocks and EfficientViT blocks to jointly capture local and global semantic information more effectively and efficiently with less information loss, while the decoder employs CNN blocks and EfficientViT blocks to catch ffner details for segmentation.
A Temporally Disentangled Contrastive Diffusion Model for Spatiotemporal Imputation
Spatiotemporal data analysis is pivotal across various domains, such as transportation, meteorology, and healthcare.
A Survey on Graph Classification and Link Prediction based on GNN
Traditional convolutional neural networks are limited to handling Euclidean space data, overlooking the vast realm of real-life scenarios represented as graph data, including transportation networks, social networks, and reference networks.
An Effective WGAN-based Anomaly Detection Model for loT Multivariate Time Series Published on Pacific-Asia Conference on Knowledge Discovery and Data Mining
This paper studies an effective unsupervised deep learning model for multivariate time series anomaly detection.
Single Image Super-Resolution Using Lightweight Networks Based on Swin Transformer
In recent years, deep learning has been applied in the field of image super-resolution reconstruction.
Personalized Heterogeneous Federated Learning with Gradient Similarity
In SPFL, the server uses the Softmax Normalized Gradient Similarity (SNGS) to weight the relationship between clients, and sends the personalized global model to each client.
RTFN: Robust Temporal Feature Network
The temporal feature networks are built to extract basic features from input data while the attentional LSTM networks are devised to capture complicated shapelets and relationships to enrich features.
STDPG: A Spatio-Temporal Deterministic Policy Gradient Agent for Dynamic Routing in SDN
Dynamic routing in software-defined networking (SDN) can be viewed as a centralized decision-making problem.
