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Found 3 papers, 2 papers with code
Harnessing Joint Rain-/Detail-aware Representations to Eliminate Intricate Rains
By integrating CoI-M with the rain-/detail-aware Contrastive learning, we develop CoIC, an innovative and potent algorithm tailored for training models on mixed datasets.
MCPI: Integrating Multimodal Data for Enhanced Prediction of Compound Protein Interactions
The identification of compound-protein interactions (CPI) plays a critical role in drug screening, drug repurposing, and combination therapy studies.
Contrastive Bayesian Analysis for Deep Metric Learning
Recent methods for deep metric learning have been focusing on designing different contrastive loss functions between positive and negative pairs of samples so that the learned feature embedding is able to pull positive samples of the same class closer and push negative samples from different classes away from each other.
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