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Found 6 papers, 2 papers with code
Enhanced Urban Region Profiling with Adversarial Contrastive Learning
Finally, we jointly optimize attentive supervised and adversarial contrastive learning to encourage the model to capture the high-level semantics of region embeddings while ignoring the noisy and irrelevant details.
Distillation Enhanced Time Series Forecasting Network with Momentum Contrastive Learning
Meanwhile, we design a supervised task to learn more robust representations and facilitate the contrastive learning process.
MEAOD: Model Extraction Attack against Object Detectors
The widespread use of deep learning technology across various industries has made deep neural network models highly valuable and, as a result, attractive targets for potential attackers.
Multi-behavior Recommendation with SVD Graph Neural Networks
Graph Neural Networks (GNNs) have been extensively employed in the field of recommendation systems, offering users personalized recommendations and yielding remarkable outcomes.
Attentive Graph Enhanced Region Representation Learning
In this paper, we propose the Attentive Graph Enhanced Region Representation Learning (ATGRL) model, which aims to capture comprehensive dependencies from multiple graphs and learn rich semantic representations of urban regions.
Sharp Eyes: A Salient Object Detector Working The Same Way as Human Visual Characteristics
In the OS branch, we first aggregate multi-level features to adaptively select complementary components, and then feed the saliency features with expanded boundary into aggregated features to guide the network obtain complete prediction.
